,db_name,id,knowledge,description,definition,type,children_knowledge
0,solar,0,Panel Performance Ratio (PPR),Measures how well a solar panel is performing relative to its rated power.,"PPR = \frac{MeasuredPowerW}{PowerRatedW} \times 100\%, \text{where MeasuredPowerW is the measured power output and PowerRatedW is the rated power of the panel.}",calculation_knowledge,-1
1,solar,1,Panel Efficiency Loss Rate (PELR),Calculates the percentage of efficiency loss per year for a solar panel.,"PELR = \frac{CurrentEfficiencyPercent - InitialEfficiencyPercent}{Years\_Since\_Installation} \times 100\%, \text{where CurrentEfficiencyPercent is obtained from efficiency_profile.current_efficiency.curreffpct.}",calculation_knowledge,-1
2,solar,2,Temperature Performance Coefficient Impact (TPCI),Quantifies the impact of temperature on panel performance based on its temperature coefficient.,"TPCI = PowerRatedW \times TempCoef \times (CellTempC - 25), \text{where TempCoef is the temperature coefficient and 25°C is the standard test condition temperature.}",calculation_knowledge,-1
3,solar,3,Energy Production Efficiency (EPE),Measures the overall energy production efficiency considering all losses.,"EPE = PPR \times (1 - SoilingLossPercent/100) \times (1 - CumulativeDegradationPercent/100), \text{where PPR is the Panel Performance Ratio.}",calculation_knowledge,[0]
4,solar,4,Inverter Efficiency Loss (IEL),Calculates the energy lost due to inverter inefficiency.,"IEL = MeasuredPowerW \times (1 - InverterEfficiencyPercent/100), \text{where InverterEfficiencyPercent is from power_metrics.inverteffpct.}",calculation_knowledge,-1
5,solar,5,Fill Factor Degradation Rate (FFDR),Calculates the rate at which the fill factor of a panel is degrading over time.,"FFDR = \frac{FillFactorInitial - FillFactorCurrent}{Years\_Since\_Installation} \times 100\%, \text{where FillFactorInitial and FillFactorCurrent are from the electrical table.}",calculation_knowledge,-1
6,solar,6,Soiling Impact Factor (SIF),Quantifies the impact of soiling on power production based on dust density.,"SIF = SoilingLossPercent / DustDensityGM2, \text{where higher values indicate greater sensitivity to dust accumulation.}",calculation_knowledge,-1
7,solar,7,Maintenance Cost Efficiency (MCE),Evaluates the cost-effectiveness of maintenance relative to the plant's capacity.,"MCE = \frac{MaintenanceCostUSD + CleaningCostUSD + ReplacementCostUSD}{GenCapMW}, \text{where lower values indicate more cost-effective maintenance.}",calculation_knowledge,-1
8,solar,8,Revenue Loss Rate (RLR),Calculates the revenue loss per MW of capacity due to maintenance issues.,"RLR = \frac{RevenueLossUSD}{GenCapMW}, \text{where higher values indicate greater financial impact from downtime.}",calculation_knowledge,-1
9,solar,9,Irradiance Utilization Ratio (IUR),Measures how effectively the panel converts available solar irradiance to power.,"IUR = \frac{MeasuredPowerW / PanelAreaM2}{POAIrradianceWM2}, \text{where POAIrradianceWM2 is from irradiance_conditions.irradiance_types[3] and PanelAreaM2 is the area of the panel.}",calculation_knowledge,-1
10,solar,10,Critical Performance Threshold,Defines when a panel's performance has degraded to a level requiring attention.,"A solar panel is considered below critical performance threshold when its Panel Performance Ratio (PPR) falls below 80% of the expected value for its age, accounting for normal degradation.",domain_knowledge,[0]
11,solar,11,Hot Spot Risk,Indicates conditions that suggest a panel may be developing hot spots.,A panel is at risk for hot spots when it shows irregular electrical parameters (Voc or Isc deviations >5% compared to other panels in the same string) combined with cell temperatures exceeding 20°C above ambient temperature.,domain_knowledge,-1
12,solar,13,Soiling Cleaning Threshold,Defines when panel cleaning should be performed based on soiling conditions.,"Cleaning should be performed when Soiling Loss Percentage exceeds 5% or when Dust Density exceeds 0.15 g/m², whichever occurs first.",domain_knowledge,-1
13,solar,14,End-of-Life Indicator,Defines criteria for determining when a panel should be considered for replacement.,A panel has reached effective end-of-life when its cumulative degradation exceeds 20% or when maintenance costs in a 12-month period exceed 30% of replacement value.,domain_knowledge,-1
14,solar,15,Optimal Performance Window,Defines the environmental conditions for optimal panel performance.,"The optimal performance window occurs when cell temperature is between 25-45°C, POA irradiance exceeds 800 W/m², and soiling loss is less than 2%.",domain_knowledge,-1
15,solar,16,Panel String Mismatch,Identifies when panels in a string have mismatched electrical characteristics.,A string has significant mismatch when the standard deviation of current measurements (Imp or Isc) across panels exceeds 3% of the mean value under the same irradiance conditions.,domain_knowledge,-1
16,solar,17,Warranty Claim Threshold,Criteria for when warranty claims should be initiated based on performance degradation.,"A warranty claim should be considered when a panel's Energy Production Efficiency falls more than 10% below the manufacturer's warranty curve for its age, with at least three consecutive measurements confirming the underperformance.",domain_knowledge,[3]
17,solar,18,Weather Severity Index,Classifies environmental conditions based on their potential impact on solar plant operations.,"A composite index calculated from extreme values of temperature, humidity, wind speed, and precipitation, where higher values indicate more severe operating conditions and increased risk of performance issues.",domain_knowledge,-1
18,solar,19,Grid Stability Factor,Measures the contribution of the inverter to grid stability.,"A metric determined by the combination of Power Quality Index, Harmonic Distortion Percentage, and Power Factor, where values closer to 1.0 indicate better contribution to grid stability.",domain_knowledge,-1
19,solar,20,PowerRatedW (Panel Rated Power),Illustrates the rated power output of solar panels.,"Measured in watts (W), representing the maximum power output a panel can produce under Standard Test Conditions (STC). Values typically range from 250W for older models to 650W for newer high-efficiency panels.",value_illustration,-1
20,solar,21,PaneEffPct (Panel Efficiency Percentage),Illustrates the efficiency of solar panels in converting sunlight to electricity.,"Expressed as a percentage, representing how much of the sun's energy hitting the panel is converted to electricity. Modern panels typically range from 15% to 23%, with premium models reaching up to 25%.",value_illustration,-1
21,solar,22,TempCoef (Temperature Coefficient),Illustrates how panel performance changes with temperature.,"Expressed as a percentage per degree Celsius (usually negative), indicating how much a panel's power output decreases for each degree above 25°C. Typical values range from -0.25% to -0.50% per °C, with lower absolute values indicating better high-temperature performance.",value_illustration,-1
22,solar,23,CellTempC (Cell Temperature),Illustrates the operating temperature of solar cells.,"Measured in degrees Celsius, representing the actual temperature of the solar cells during operation. Typically ranges from 40°C to 70°C depending on ambient conditions and panel design, with temperatures above 80°C potentially causing accelerated degradation.",value_illustration,-1
23,solar,24,FillFactor (Fill Factor),Illustrates the ratio of actual maximum power to theoretical power.,A dimensionless value between 0 and 1 representing the ratio of maximum power point (Vmp × Imp) to open-circuit voltage times short-circuit current (Voc × Isc). High-quality commercial panels typically have fill factors between 0.75 and 0.85.,value_illustration,-1
24,solar,25,SoilingLossPct (Soiling Loss Percentage),Illustrates the power loss due to dirt and dust accumulation.,"Expressed as a percentage of power output lost due to soiling. Clean panels have values near 0%, while heavily soiled panels in dusty environments may experience losses exceeding 15%. Typical values in maintained systems range from 2% to 5%.",value_illustration,-1
25,solar,26,AnnDegRate (Annual Degradation Rate),Illustrates the yearly performance degradation of solar panels.,"Expressed as a percentage, representing how much a panel's output decreases per year. Quality silicon panels typically degrade at 0.5% to 0.7% annually, while lower quality or certain thin-film technologies may degrade at rates above 1% annually.",value_illustration,-1
26,solar,27,CumDegPct (Cumulative Degradation Percentage),Illustrates the total performance loss over a panel's lifetime.,"Expressed as a percentage, representing total performance degradation since installation. New panels start at 0%, while panels approaching end-of-life may have values of 15-20% or higher, with manufacturer warranties typically covering degradation up to 20% over 25 years.",value_illustration,-1
27,solar,28,POAIrradianceWM2 (Plane-of-Array Irradiance),Illustrates the solar energy available to panels in their installed orientation.,"Measured in watts per square meter (W/m²), representing the solar energy reaching the panel surface. Typical daytime values range from 200 W/m² on heavily overcast days to 1000+ W/m² under clear sky conditions at solar noon.",value_illustration,-1
28,solar,29,InvertEffPct (Inverter Efficiency Percentage),Illustrates the efficiency of inverters in converting DC to AC power.,"Expressed as a percentage, representing how much DC power from panels is successfully converted to AC power. Modern string inverters typically operate at 96-98% efficiency, while microinverters may range from 94-97% efficiency.",value_illustration,-1
29,solar,30,Temperature Adjusted Performance Ratio (TAPR),Enhances the Panel Performance Ratio by accounting for temperature effects.,"TAPR = PPR + (TPCI / PowerRatedW), where PPR is the Panel Performance Ratio and TPCI is the Temperature Performance Coefficient Impact.",calculation_knowledge,"[0, 2]"
30,solar,31,Total System Loss (TSL),Calculates the combined power losses from all major sources.,"TSL = (PowerRatedW × CumDegPct/100) + (MeasuredPowerW × SoilingLossPct/100) + IEL, where IEL is the Inverter Efficiency Loss and CumDegPct is from efficiency_profile.degradation.cumdegpct.",calculation_knowledge,"[4, 27]"
31,solar,32,Maintenance Return on Investment (MROI),Evaluates the financial return of maintenance activities.,"MROI = RLR / MCE, where RLR is the Revenue Loss Rate and MCE is the Maintenance Cost Efficiency. Higher values indicate better return on maintenance investments.",calculation_knowledge,"[7, 8]"
32,solar,33,Effective Performance Index (EPI),Comprehensive metric that accounts for all factors affecting panel performance.,"EPI = PPR × (1 - TSL/PowerRatedW) × (IUR/PaneEffPct), where PPR is the Panel Performance Ratio, TSL is the Total System Loss, and IUR is the Irradiance Utilization Ratio.",calculation_knowledge,"[0, 9, 31]"
33,solar,34,Normalized Degradation Index (NDI),Compares panel degradation to expected rates based on panel type and age.,"NDI = PELR / AnnDegRate, where PELR is the Panel Efficiency Loss Rate and AnnDegRate is from efficiency_profile.degradation.anndegrate. Values above 1.0 indicate faster than expected degradation.",calculation_knowledge,"[1, 26]"
34,solar,35,Weather Corrected Efficiency (WCE),Adjusts panel efficiency measurements to account for weather conditions.,"WCE = CurrentEfficiencyPercent × (1 + TempCoef × (25 - CellTempC) / 100) × (1000 / POAIrradianceWM2), where CurrentEfficiencyPercent is from efficiency_profile.current_efficiency.curreffpct.",calculation_knowledge,"[22, 23, 28]"
35,solar,36,Expected Energy Yield (EEY),Calculates the expected energy production considering current conditions.,"EEY = PowerRatedW × EPE × SIF × POAIrradianceWM2 / 1000, where EPE is the Energy Production Efficiency and SIF is the Soiling Impact Factor.",calculation_knowledge,"[3, 6, 28]"
36,solar,37,Grid Integration Quality (GIQ),Measures the overall quality of power delivered to the grid.,"GIQ = PWRQualIDX × (1 - power_metrics.harmdistpct/100) × power_metrics.invertpowfac, using PWRQualIDX from the inverter table.",calculation_knowledge,[19]
37,solar,38,Electrical Degradation Index (EDI),Measures the combined degradation of electrical parameters.,"EDI = (FillFactorInitial - FillFactorCurrent)/FillFactorInitial + (VocInitialV - VocCurrV)/VocInitialV + (IscInitialA - IscCurrA)/IscInitialA, where higher values indicate more severe degradation.",calculation_knowledge,[24]
38,solar,39,Financial Impact of Degradation (FID),Calculates the financial cost of degradation over time.,"FID = GenCapMW × 1000 × NDI × PELR × ElectricityPricePerKWh × 24 × 365, where NDI is the Normalized Degradation Index and PELR is the Panel Efficiency Loss Rate.",calculation_knowledge,"[1, 34]"
39,solar,40,Advanced Performance Degradation Alert,Identifies panels showing accelerated degradation requiring intervention.,A panel requires urgent attention when its Normalized Degradation Index exceeds 1.5 and its TAPR is below the Critical Performance Threshold.,domain_knowledge,"[10, 30, 34]"
40,solar,41,Premium Maintenance Candidate,Identifies panels that would benefit most from premium maintenance services.,A panel is considered a premium maintenance candidate when its Maintenance Return on Investment exceeds 2.0 and its Energy Production Efficiency is below 90% but above 75%.,domain_knowledge,"[3, 32]"
41,solar,42,Optimal Cleaning Schedule,Determines the ideal cleaning frequency based on environmental conditions and soiling rates.,Cleaning should be scheduled when SIF × DustDensityGM2 exceeds the Soiling Cleaning Threshold or when Expected Energy Yield is reduced by more than 3% due to soiling.,domain_knowledge,"[6, 13, 36]"
42,solar,43,High-Risk Weather Condition,Identifies weather patterns that pose significant risk to panel performance or longevity.,Weather conditions are considered high-risk when the Weather Severity Index exceeds 7.0 and Cell Temperature exceeds the upper limit of the Optimal Performance Window.,domain_knowledge,"[15, 18, 23]"
43,solar,44,End-of-Warranty Optimization,Strategy for optimizing panel replacements near warranty expiration.,Panels should be evaluated for warranty claims when approaching warranty expiration if their Normalized Degradation Index exceeds 0.9 or if they fail to meet the Warranty Claim Threshold criteria.,domain_knowledge,"[17, 34]"
44,solar,45,Grid Export Quality Classification,Classification system for the quality of power exported to the grid.,"Power export quality is classified as 'Premium' when Grid Integration Quality exceeds 0.95, 'Standard' when between 0.90 and 0.95, and 'Substandard' when below 0.90, with substandard exports potentially subject to utility penalties.",domain_knowledge,[37]
45,solar,46,System Upgrade Candidate,Identifies plants that would benefit most from component upgrades.,A plant qualifies as an upgrade candidate when the Financial Impact of Degradation exceeds 10% of replacement cost and Effective Performance Index is below 0.85 for three consecutive months.,domain_knowledge,"[33, 39]"
46,solar,47,Inverter-Panel Compatibility Index,Assesses the compatibility between panels and their connected inverters.,An inverter-panel combination is considered optimally compatible when the panel's Weather Corrected Efficiency stays within 5% of the manufacturer's specifications and the inverter's Inverter Efficiency Percentage exceeds 97%.,domain_knowledge,"[29, 35]"
47,solar,48,Environmental Stress Classification,Categorizes the level of environmental stress a panel is experiencing.,"Environmental stress is classified based on combining Weather Severity Index and exposure time outside the Optimal Performance Window, with high stress potentially accelerating the Panel Efficiency Loss Rate.",domain_knowledge,"[1, 15, 18]"
48,solar,49,Total Economic Performance,Holistic economic assessment of a solar installation.,"A comprehensive economic evaluation combining Maintenance Return on Investment, Financial Impact of Degradation, and revenue generation adjusted by the Effective Performance Index, providing a single metric for financial performance.",domain_knowledge,"[32, 33, 39]"
49,solar,50,Maintenance Urgency Classification,Four-tier system prioritizing maintenance actions based on combined financial and operational factors,URGENT: Having critical alerts AND MROI>2.0; HIGH: Having critical alerts; MEDIUM: MROI>2.0; LOW: All other cases,domain_knowledge,[32]
50,solar,51,Cleaning Triggers,Combined conditions that determine when solar panel cleaning is economically justified.,A panel cleaning should be triggered when either: (1) meet Soiling Cleaning Threshold (2) >30 days since last cleaning—whichever occurs first.,domain_knowledge,[13]
51,solar,52,Degradation Severity Classification,"Defines thresholds for high, moderate, and normal degradation based on NDI value.","Panels are classified as 'High Degradation' if NDI exceeds 1.5, 'Moderate Degradation' if NDI is between 1.0 and 1.5, and 'Normal Degradation' if below 1.0.",domain_knowledge,[34]
52,solar,53,Alert Specification Protocol,Comprehensive rules for generating performance alerts,"Mandates that critical alerts must: (1) Reference the plant ID, panel ID and performance record; (2) Set status to 'Critical'; (3) Assign 'High' maintenance priority; (4) Set replacement priority to 'High' if performance <60% of expected, otherwise 'Medium'; (5) Mark optimization potential as 'High'; (6) Use 'ALERT_' prefix with random hash for IDs; (7) Update existing alerts within 30-day window rather than creating duplicates",domain_knowledge,-1
53,archeology,0,Scan Resolution Index (SRI),A sophisticated compound index measuring the overall resolution quality of a scan based on resolution and point density.,"SRI = \frac{\log_{10}(ScanResolMm \times 10^3)}{\log_{10}(PointDense)} \times 5, \text{ where lower values indicate higher quality resolution and more balanced scanning parameters.}",calculation_knowledge,-1
54,archeology,1,Scan Coverage Effectiveness (SCE),Measures how effectively a scan covers its target area considering both coverage percentage and overlap redundancy.,"SCE = CoverPct \times \left(1 + \frac{LapPct}{100} \times \left(1 - \frac{CoverPct}{100}\right)\right), \text{ where higher values indicate more effective coverage with appropriate overlap.}",calculation_knowledge,-1
55,archeology,2,Point Cloud Density Ratio (PCDR),"Evaluates the relationship between total points and cloud density, used to assess scan efficiency and data distribution.","PCDR = \frac{TotalPts}{CloudDense \times AreaM2}, \text{ where higher values suggest more efficient and spatially consistent scanning techniques.}",calculation_knowledge,-1
56,archeology,3,Scan Quality Score (SQS),"Comprehensive quality metric combining resolution, coverage, and noise factors with weighted importance.","SQS = \left(\frac{10}{SRI}\right)^{1.5} \times \left(\frac{SCE}{100}\right) \times \left(1 - \frac{NoiseDb}{30}\right)^2, \text{ where higher values indicate exponentially better overall scan quality with emphasis on resolution.}",calculation_knowledge,"[0, 1]"
57,archeology,4,Mesh Complexity Ratio (MCR),"Measures the topological complexity of a mesh relative to its resolution, helping identify overly complex or simplified archaeological models.","MCR = \frac{FacetFaces}{FacetVerts \times FacetResMm^2} \times 10^3, \text{ where higher values indicate more complex meshes for a given resolution, capturing finer archaeological details.}",calculation_knowledge,-1
58,archeology,5,Texture Density Index (TDI),Evaluates the pixel density of textures relative to mesh resolution for assessing surface detail preservation.,"TDI = \frac{TexPix}{\sqrt{FacetFaces} \times FacetResMm} \times 10^{-2}, \text{ where higher values indicate more detailed textures relative to geometric complexity.}",calculation_knowledge,-1
59,archeology,6,Model Fidelity Score (MFS),"Combines mesh complexity, texture quality, and geometric accuracy to assess overall 3D model fidelity for archaeological analysis.","MFS = MCR \times \left(\frac{TDI}{10}\right) \times \left(1 + \exp\left(-GeomDeltaMm\right)\right), \text{ where higher values indicate more accurate and detailed models with appropriate complexity.}",calculation_knowledge,"[4, 5]"
60,archeology,7,Environmental Suitability Index (ESI),Evaluates how suitable environmental conditions were for scanning operations using weighted parameters.,"ESI = 100 - 2.5 \times \left|AmbicTemp - 20\right| - \left|\frac{HumePct - 50}{2}\right|^{1.5} - \frac{600}{IllumeLux + 100}, \text{ where higher values indicate more ideal scanning conditions adjusted for relative importance.}",calculation_knowledge,-1
61,archeology,8,Processing Efficiency Ratio (PER),Measures the efficiency of scan processing by comparing processing time to data complexity and size.,"PER = \frac{GBSize \times \log_{10}(TotalPts)}{FlowHrs \times (ProcCPU + ProcGPU)/200}, \text{ where higher values indicate more efficient processing relative to data complexity.}",calculation_knowledge,-1
62,archeology,9,Archaeological Documentation Completeness (ADC),Comprehensive score for how completely a site has been documented through scanning with weighted importance factors.,"ADC = \left(SQS \times 0.4\right) + \left(MFS \times 0.4\right) + \left(SCE \times 0.2\right) - 5 \times \sqrt{\frac{NoiseDb}{10}}, \text{ where higher values indicate more complete documentation with multiple quality factors.}",calculation_knowledge,"[3, 6, 1]"
63,archeology,10,High Resolution Scan,Defines what constitutes a high-resolution archaeological scan based on quantitative parameters.,"A scan with ScanResolMm \leq 1.0 and PointDense \geq 1000, allowing for sub-millimeter precision in archaeological documentation and feature detection.",domain_knowledge,-1
64,archeology,11,Comprehensive Coverage,Defines the standard for comprehensive scan coverage of an archaeological site or artifact with statistical confidence.,"A scan with CoverPct \geq 95 and LapPct \geq 30, ensuring minimal data gaps and sufficient overlap for accurate registration with 95% confidence interval for spatial measurements.",domain_knowledge,-1
65,archeology,12,Premium Quality Scan,Defines the criteria for a premium quality archaeological scan suitable for conservation planning and scholarly publication.,"A scan that is both a High Resolution Scan and has Comprehensive Coverage with SQS > 7.5, where SQS is the Scan Quality Score, producing data suitable for detailed analysis and conservation planning.",domain_knowledge,"[10, 11, 3]"
66,archeology,13,High Fidelity Mesh,Defines criteria for high-fidelity 3D mesh models in archaeological documentation suitable for analytical studies.,"A mesh with MCR > 5.0, FacetResMm < 1.0, and GeomDeltaMm < 0.5, where MCR is the Mesh Complexity Ratio, capable of representing fine archaeological details and surface morphology.",domain_knowledge,[4]
67,archeology,14,Degradation Risk Zone,Identifies archaeological sites at risk of degradation requiring urgent conservation intervention based on multiple factors.,"A site with PresStat containing 'Poor' or 'Critical' and StructState not containing 'Stable', signaling immediate conservation needs due to active deterioration processes.",domain_knowledge,[26]
68,archeology,15,Optimal Scanning Conditions,Defines the environmental conditions considered optimal for archaeological scanning based on instrument sensitivity profiles.,"Conditions with ESI > 85, where ESI is the Environmental Suitability Index (knowledge #7), characterized by moderate temperature, humidity around 50%, and good illumination, minimizing environmental interference with scanning accuracy.",domain_knowledge,[7]
69,archeology,16,Digital Conservation Priority,"Classification system for prioritizing digital conservation efforts based on site conditions, historical significance, and preservation status.","A scoring system where sites in Degradation Risk Zones with GuessDate older than 1000 BCE or with TypeSite = 'Rare' or 'Unique' receive highest priority for digital preservation through Premium Quality Scans, requiring immediate allocation of scanning resources.",domain_knowledge,"[12, 14]"
70,archeology,17,Processing Bottleneck,Identifies processing workflows that are experiencing resource constraints using performance metrics.,"A processing record with PER < 0.5, where PER is the Processing Efficiency Ratio, indicating potential hardware limitations affecting processing speed and output quality, requiring workflow optimization.",domain_knowledge,[8]
71,archeology,18,Registration Quality Threshold,Defines the quality threshold for scan registration in archaeological documentation based on error propagation analysis.,"A registration with LogAccuMm < 1.0 and ErrValMm < 2.0, ensuring sufficient accuracy for reliable spatial analysis with maximum tolerable error below the significant feature size threshold.",domain_knowledge,-1
72,archeology,19,Full Archaeological Digital Twin,Defines the comprehensive digital representation of an archaeological site meeting all quality standards for research and preservation.,"A site with Premium Quality Scans, High Fidelity Mesh, Registration Quality Threshold met, and ADC > 85, where ADC is Archaeological Documentation Completeness, representing a complete digital twin suitable for research, conservation, and visualization purposes.",domain_knowledge,"[12, 13, 18, 9]"
73,archeology,20,ScanResolMm (Scan Resolution),Illustrates the significance of scan resolution measurements in archaeological scanning for feature detection.,"Measured in millimeters, representing the smallest feature that can be distinguished in the scan. Values like 0.5mm enable documentation of fine tool marks on artifacts, while 2.0mm might only capture general shape and macroscopic features.",value_illustration,-1
74,archeology,21,PointDense (Point Density),Illustrates the significance of point density in archaeological point clouds for information richness.,"Measured as points per square meter. Values around 100 capture basic site topography, 1,000 can document structural details, while 10,000+ enables analysis of surface textures and fine engravings across multiple scales of inquiry.",value_illustration,-1
75,archeology,22,NoiseDb (Noise Level),Illustrates the impact of noise levels in point cloud data on feature recognition accuracy.,"Measured in decibels, representing signal-to-noise ratio in scan data. Values below 1.0 indicate clean data suitable for detailed analysis, while values above 3.0 suggest significant noise that may obscure small features and introduce measurement uncertainty.",value_illustration,-1
76,archeology,23,CoverPct (Coverage Percentage),Illustrates the significance of coverage percentage in archaeological scans for site completeness assessment.,"Percentage of target area successfully captured in scan data. Values above 95% indicate near-complete documentation, while 80% might have significant gaps requiring additional scanning or interpolation methods for comprehensive site analysis.",value_illustration,-1
77,archeology,24,GeomDeltaMm (Geometric Accuracy),Illustrates the significance of geometric accuracy in 3D models for measurement reliability.,"Measured in millimeters, representing the average deviation between the scan data and final 3D model. Values below 0.1mm indicate museum-quality accuracy, while values around 1.0mm are suitable for general documentation but introduce uncertainty in fine feature analysis.",value_illustration,-1
78,archeology,25,PhaseFactor (Cultural Period),Illustrates the significance of cultural period classification in archaeological sites.,"Classifies archaeological sites into standardized chronological/cultural periods. Values like 'Neolithic' (10,000-4,500 BCE), 'Bronze Age' (3,300-1,200 BCE), 'Roman' (27 BCE-476 CE), or 'Medieval' (476-1453 CE) determine applicable research methodologies, conservation approaches, and contextual interpretation frameworks.",value_illustration,-1
79,archeology,26,StructState (Structural State),Illustrates structural state classifications in archaeological conservation.,"A categorical assessment with specific values: 'Stable' indicates structures that maintain integrity under normal conditions, 'Unstable' indicates structures showing signs of deterioration requiring intervention, and 'Critical' indicates structures at imminent risk of collapse requiring emergency stabilization.",value_illustration,-1
80,archeology,27,FlowStage (Processing Stage),Illustrates the progression of data processing in archaeological scanning workflows.,"A sequential classification system with defined stages: 'Raw' (unprocessed scan data), 'Aligned' (multiple scans registered together), 'Cleaned' (noise and artifacts removed), 'Meshed' (point cloud converted to polygon mesh), and 'Textured' (surface textures applied to mesh). Each stage represents a discrete processing milestone.",value_illustration,-1
81,archeology,28,LogMethod (Registration Method),Illustrates different scan registration methodologies in archaeological documentation.,"A categorization of alignment techniques with specific methodologies: 'ICP' (Iterative Closest Point algorithm for point cloud alignment), 'Target-based' (alignment using physical reference markers), 'Hybrid' (combination of automatic and manual alignment), and 'SLAM' (Simultaneous Localization and Mapping for real-time registration). Each method has distinct accuracy characteristics and use cases.",value_illustration,-1
82,archeology,29,GuessDate (Estimated Dating),Illustrates dating conventions in archaeological classification for chronological placement.,"Values like '3500-3000 BCE', '1st c. CE', or 'ca. 1450 CE' represent estimated chronological placement based on excavation findings. Precision varies from specific years to century-level estimates depending on available evidence and dating methodologies employed.",value_illustration,-1
83,archeology,30,Scan Time Efficiency (STE),Measures how efficiently scanning time was used relative to data quality and completeness metrics.,"STE = \frac{SQS \times \sqrt{CoverPct}}{SpanMin \times \sqrt{ScanCount}}, \text{ where SQS is the Scan Quality Score and higher STE values indicate more efficient use of scanning time relative to coverage achieved.}",calculation_knowledge,[3]
84,archeology,31,Environmental Impact Factor (EIF),Quantifies how environmental conditions affected scan quality using statistical correlation analysis.,"EIF = \frac{SQS}{\text{ESI} + 10} \times 100, \text{ where SQS is the Scan Quality Score and ESI is the Environmental Suitability Index. Values closer to 100 indicate minimal environmental interference with data acquisition.}",calculation_knowledge,"[3, 7]"
85,archeology,32,Feature Extraction Efficiency (FEE),Measures the efficiency of feature identification in scan data relative to point cloud density and complexity.,"FEE = \frac{TraitCount + ArtiCount}{PCDR \times \sqrt{CloudDense}} \times 10^3, \text{ where PCDR is the Point Cloud Density Ratio and higher values indicate more effective feature extraction from point cloud data relative to spatial distribution efficiency.}",calculation_knowledge,[2]
86,archeology,33,Registration Accuracy Ratio (RAR),Evaluates registration accuracy relative to scan resolution using propagation of uncertainty principles.,"RAR = \frac{ScanResolMm}{LogAccuMm \times \sqrt{1 + \frac{ErrValMm}{LogAccuMm}}}, \text{ where values > 1 indicate registration accuracy exceeds scan resolution, a desirable outcome for precise spatial analysis.}",calculation_knowledge,-1
87,archeology,34,Spatial Density Index (SDI),Assesses point cloud density relative to site dimensions for spatial sampling adequacy.,SDI = \frac{TotalPts}{AreaM2 \times 10^4} \times \left(\frac{PointDense}{CloudDense}\right)^{0.5},calculation_knowledge,-1
88,archeology,35,Conservation Priority Index (CPI),"Quantifies the urgency of conservation efforts based on site condition, historical significance and structural stability.","CPI = \begin{cases} 100 - PS + AF \times \left(1 + \frac{TS}{10}\right), & \text{if in a Degradation Risk Zone} \\ 50 - PS + AF \times \left(1 + \frac{TS}{20}\right), & \text{otherwise} \end{cases}, \text{ where PS is 0-100 based on PresStat condition ('Excellent'=10, 'Good'=30, 'Fair'=50, 'Poor'=70, 'Critical'=90), AF is approximate age in millennia derived from GuessDate, and TS is 0-10 based on TypeSite rarity.}",calculation_knowledge,"[14, 29]"
89,archeology,36,Mesh-to-Point Ratio (MPR),Evaluates the efficiency of mesh generation from point cloud data for optimal decimation determination.,"MPR = \frac{FacetVerts}{TotalPts} \times 100 \times \left(\frac{MCR}{10}\right)^{0.3}, \text{ where MCR is the Mesh Complexity Ratio and values around 25-30 indicate optimal decimation for archaeological purposes with appropriate feature preservation.}",calculation_knowledge,[4]
90,archeology,37,Processing Resource Utilization (PRU),Measures the efficiency of computing resource utilization during scan processing relative to data complexity.,"PRU = \frac{FlowHrs \times (ProcCPU + ProcGPU) / 2}{GBSize \times 10 \times \log_{10}(FacetVerts + 10^4)}, \text{ where lower values indicate more efficient use of computing resources relative to mesh complexity.}",calculation_knowledge,-1
91,archeology,38,Digital Preservation Quality (DPQ),Comprehensive metric for evaluating digital preservation quality for archaeological sites with weighted quality factors.,"DPQ = (0.3 \times ADC) + (0.3 \times MFS) + (0.2 \times RAR) + (0.2 \times SCE) - 2 \times \sqrt{\frac{ErrValMm}{ScanResolMm}}, \text{ where ADC is Archaeological Documentation Completeness, MFS is Model Fidelity Score, RAR is Registration Accuracy Ratio, and SCE is Scan Coverage Effectiveness.}",calculation_knowledge,"[9, 6, 33, 1]"
92,archeology,39,Equipment Effectiveness Ratio (EER),Evaluates how effectively equipment was utilized based on power consumption and scan quality relative to equipment capability.,"EER = \frac{SQS \times EquipStatus\_value}{PowerLevel \times (101 - EquipAge\_days) / 365} \times 25, \text{ where SQS is the Scan Quality Score, EquipStatus_value is 1.0 for 'Excellent' to 0.2 for 'Poor', and EquipAge_days is days since EquipTune, with higher values indicating more efficient use of equipment relative to condition.}",calculation_knowledge,[3]
93,archeology,40,Spatially Complex Site,Defines sites with complex spatial characteristics requiring specialized scanning approaches based on dimensional analysis.,"A site with AreaM2 > 100 and SDI > 50, where SDI is the Spatial Density Index, requiring strategic planning for comprehensive documentation with multiple scanning stations and methodologies to capture complex spatial relationships.",domain_knowledge,[34]
94,archeology,41,Texture-Critical Artifact,Identifies artifacts where texture documentation is critical for analysis based on surface morphology characteristics.,"Features with TextureStudy containing 'Detailed' or 'Critical' and TDI > 8.0, where TDI is the Texture Density Index, requiring specialized imaging techniques such as photometric stereo or multi-spectral imaging for complete surface characterization.",domain_knowledge,[5]
95,archeology,42,Conservation Emergency,Identifies sites requiring immediate conservation intervention based on multiple risk factors and structural assessment.,"A site that is in a Degradation Risk Zone with CPI > 75, where CPI is the Conservation Priority Index, requiring immediate protective measures and priority documentation with at least Premium Quality Scans before any intervention to establish baseline condition.",domain_knowledge,"[14, 35]"
96,archeology,43,Processing Optimized Workflow,Defines optimized processing workflows balancing quality and resource use through benchmarked performance metrics.,"A processing workflow with PRU < 5.0 while maintaining MFS > 7.0, where PRU is Processing Resource Utilization and MFS is Model Fidelity Score, representing an efficient balance of resource use and output quality through optimized algorithm selection and hardware allocation.",domain_knowledge,"[37, 6]"
97,archeology,44,Registration Confidence Level,Classification system for registration confidence based on multiple factors and error propagation analysis.,"A classification where 'High Confidence' registrations have RAR > 1.5 and LogMethod containing 'Target', where RAR is Registration Accuracy Ratio, 'Medium Confidence' have RAR between 1.0-1.5, and 'Low Confidence' have RAR < 1.0, determining appropriate use cases for spatial analysis and interpretive visualization.",domain_knowledge,[33]
98,archeology,45,Environmental Challenge Scan,Identifies scans conducted under challenging environmental conditions requiring expertise and specialized equipment adaptation.,"A scan with EIF > 120, where EIF is Environmental Impact Factor, indicating successful data capture despite suboptimal environmental conditions through adaptive scanning methodologies and operator expertise in field condition compensation.",domain_knowledge,[31]
99,archeology,46,High Temporal Value Site,Identifies sites with exceptional historical significance based on age and context for prioritized research attention.,"A site with GuessDate containing dates before 500 CE and CPI > 60, where CPI is Conservation Priority Index, representing locations of exceptional chronological significance requiring specialized documentation protocols to capture temporally significant features.",domain_knowledge,"[35, 29]"
100,archeology,47,Resource-Intensive Model,Identifies 3D models requiring substantial computing resources for visualization and analysis based on complexity metrics.,"A model with FacetFaces > 2,000,000 and MPR < 15, where MPR is Mesh-to-Point Ratio, requiring specialized hardware for effective interaction and analytical software optimized for large-scale geometric processing with hierarchical level-of-detail implementation.",domain_knowledge,[36]
101,archeology,48,Multi-Phase Documentation Project,Defines complex archaeological projects requiring multiple scanning phases with integrated documentation strategy.,"A project with multiple scans where the total ADC < 70 for individual scans but DPQ > 80 when combined, where ADC is Archaeological Documentation Completeness and DPQ is Digital Preservation Quality, indicating comprehensive documentation achieved through multiple phases with coherent registration strategy for holistic interpretation.",domain_knowledge,"[9, 38]"
102,archeology,49,Equipment Optimization Opportunity,Identifies scenarios where equipment settings could be optimized for better results based on performance analysis.,"Scanning scenarios where EER < 30 but ESI > 80, where EER is Equipment Effectiveness Ratio and ESI is Environmental Suitability Index, indicating potential for improved equipment utilization in favorable conditions through calibration adjustment and scanning parameter optimization.",domain_knowledge,"[39, 7]"
103,archeology,50,Environmental Condition Classification System (ECCS),A comprehensive classification system for archaeological site environments based on their suitability for scanning operations.,"A four-tier classification where 'Optimal Scanning Conditions' have ESI > 85, 'Good Scanning Conditions' have ESI between 70-85, 'Acceptable Scanning Conditions' have ESI between 50-70, and 'Challenging Scanning Conditions' have ESI < 50. This classification guides scanning schedule planning and equipment selection to maximize data quality.",domain_knowledge,"[7, 15]"
104,archeology,51,Workflow Efficiency Classification,A standardized categorization system for assessing processing workflow efficiency based on Processing Resource Utilization (PRU) values.,"A three-tier classification where 'Optimized' workflows have PRU < 5.0 (highly efficient resource usage), 'Acceptable' workflows have PRU between 5.0-10.0 (reasonable efficiency), and 'Needs Optimization' workflows have PRU > 10.0 (inefficient resource usage requiring intervention). This classification guides processing workflow improvements and resource allocation decisions.",domain_knowledge,[37]
105,archeology,52,Risk Zone Category,Classification system that evaluates archaeological sites for degradation risk based on preservation status and structural condition.,Categorizes archaeological sites into two main groups: 'Degradation Risk Zone' and 'Not in Risk Zone'. 'Not in Risk Zone' means that the site is not in a Degradation Risk Zone.,domain_knowledge,"[14, 26]"
106,archeology,53,Mesh Quality Classification,A standardized system for categorizing archaeological site documentation based on the presence and quality of 3D mesh models.,"A three-tier classification where 'Has High-Fidelity Meshes' indicates sites with at least one mesh meeting high-fidelity criteria, 'Standard Mesh Quality' indicates sites with meshes that don't meet high-fidelity standards, and 'No Mesh Data' indicates sites lacking 3D mesh documentation entirely. This classification helps prioritize additional documentation efforts and determines appropriate analytical approaches for different sites.",domain_knowledge,[13]
107,fake,0,Account Activity Frequency (AAF),Measures how frequently an account engages in platform activities relative to its age.,AAF = \frac{\text{sesscount}}{\text{acctagespan}},calculation_knowledge,-1
108,fake,1,Content Authenticity Score (CAS),Aggregates multiple authenticity indicators into a single score.,"CAS = 0.3 \times \text{authenscore} + 0.3 \times \text{cntuniqscore} + 0.4 \times \text{convnatval} where values are normalized to [0,1]",calculation_knowledge,-1
109,fake,2,Network Growth Velocity (NGV),Measures the rate of network growth considering both followers and following.,NGV = \sqrt{\text{followgrowrate}^2 + \text{followinggrowrate}^2},calculation_knowledge,-1
110,fake,3,Bot Behavior Index (BBI),Combines multiple bot-detection metrics into a single score.,BBI = 0.4 \times \text{botlikscore} + 0.3 \times \text{autobehavscore} + 0.3 \times (1 - \text{convnatval}),calculation_knowledge,-1
111,fake,4,Security Risk Score (SRS),Calculates overall security risk based on multiple factors.,SRS = 0.4 \times \text{riskval} + 0.3 \times (1 - \text{trustval}) + 0.3 \times \text{impactval},calculation_knowledge,-1
112,fake,5,Profile Credibility Index (PCI),Evaluates overall profile credibility.,PCI = 0.3 \times \text{credscore} + 0.3 \times \text{reputscore} + 0.4 \times \text{completeness},calculation_knowledge,-1
113,fake,6,Coordinated Activity Score (CAS),Measures likelihood of coordinated behavior across accounts.,CAS = 0.5 \times \text{coordscore} + 0.3 \times \text{netinflscore} + 0.2 \times \frac{\text{clustsize}}{100},calculation_knowledge,-1
114,fake,7,Technical Evasion Index (TEI),Quantifies attempts to evade detection.,TEI = 0.4 \times \text{vpnratio} + 0.3 \times \frac{\text{proxycount}}{10} + 0.3 \times \frac{\text{ipcountrynum}}{20},calculation_knowledge,-1
115,fake,8,Content Manipulation Score (CMS),Evaluates content manipulation patterns.,CMS = 0.4 \times (1 - \text{cntuniqscore}) + 0.3 \times \text{mediareratio} + 0.3 \times (1 - \text{txtuniq}),calculation_knowledge,-1
116,fake,9,Moderation Priority Score (MPS),Calculates priority for moderation review.,MPS = 0.3 \times \frac{\text{abuserepnum}}{1000} + 0.4 \times \text{impactval} + 0.3 \times \text{riskval},calculation_knowledge,-1
117,fake,10,High-Risk Account,Identifies accounts requiring immediate attention.,An account with SRS > 0.8 and at least one active security detection with threatlvl = 'Critical',domain_knowledge,[4]
118,fake,11,Bot Network,Identifies coordinated bot activity.,A cluster where clustsize > 10 and average BBI > 0.7 for all accounts in cluster,domain_knowledge,[3]
119,fake,12,Trusted Account,Identifies highly trustworthy accounts.,An account with PCI > 0.8 and no security detections in the past 180 days,domain_knowledge,[5]
120,fake,13,Content Farm,Identifies accounts mass-producing similar content.,An account with CMS > 0.7 and postfreq > 50 posts per day,domain_knowledge,[8]
121,fake,14,Sockpuppet Network,Identifies related accounts used for manipulation.,A group of accounts where linkacctnum > 5 and CAS > 0.8,domain_knowledge,[6]
122,fake,15,Dormant Bot,Identifies inactive bot accounts.,An account with acctstatus = 'Dormant'.,domain_knowledge,[3]
123,fake,16,VPN Abuser,Identifies accounts systematically using VPNs.,An account with TEI > 0.8 and at least 3 different countries in login locations,domain_knowledge,[7]
124,fake,17,Engagement Manipulator,Identifies artificial engagement patterns.,An account where engauth < 0.3 and tempinteractpat = 'Automated',domain_knowledge,-1
125,fake,18,Serial Violator,Identifies repeat policy violators.,An account with susphist > 2 and warnnum > 5,domain_knowledge,-1
126,fake,19,Amplification Network,Identifies coordinated content amplification.,A cluster where clustrole = 'Amplifier' and coordscore > 0.8,domain_knowledge,-1
127,fake,20,detection_score_profile.overall.confval,Illustrates confidence value in detection scores.,"Ranges from 0 to 1. Values above 0.8 indicate high-confidence detections, while values below 0.3 suggest uncertain results requiring manual review.",value_illustration,-1
128,fake,21,moderationaction.coordscore,Illustrates coordination score meaning.,"Ranges from 0 to 1. Scores above 0.7 strongly indicate coordinated behavior, while scores below 0.2 suggest independent actions.",value_illustration,-1
129,fake,22,networkmetrics.network_engagement_metrics.engagement_metrics.engauth,Illustrates engagement authenticity score.,"Ranges from 0 to 1. Scores above 0.9 indicate highly authentic engagement, while scores below 0.4 suggest artificial or automated engagement.",value_illustration,-1
130,fake,23,contentbehavior.cntuniqscore,Illustrates content uniqueness scoring.,"Ranges from 0 to 1. Scores above 0.8 indicate highly unique content, while scores below 0.3 suggest duplicate or templated content.",value_illustration,-1
131,fake,24,messaginganalysis.convnatval,Illustrates conversation naturalness value.,"Ranges from 0 to 1. Values above 0.7 indicate natural human conversation, while values below 0.3 suggest automated or scripted responses.",value_illustration,-1
132,fake,25,technicalinfo.iprepscore,Illustrates IP reputation scoring.,"Ranges from 0 to 1. Scores above 0.8 indicate trusted IPs, while scores below 0.3 suggest potentially malicious or compromised IPs.",value_illustration,-1
133,fake,26,profile_composition.completeness,Illustrates profile completeness scoring.,"Ranges from 0 to 1. Values above 0.8 indicate well-maintained profiles, while values below 0.4 suggest placeholder or abandoned profiles.",value_illustration,-1
134,fake,27,moderationaction.trustval,Illustrates trust value meaning.,"Ranges from 0 to 1. Values above 0.7 indicate highly trusted accounts, while values below 0.3 suggest untrusted or suspicious accounts.",value_illustration,-1
135,fake,28,moderationaction.impactval,Illustrates impact value meaning.,"Ranges from 0 to 1. Values above 0.7 indicate high-impact violations requiring immediate attention, while values below 0.3 suggest low-priority issues.",value_illustration,-1
136,fake,29,detection_score_profile.behavior_scores.botlikscore,Illustrates bot likelihood scoring.,"Ranges from 0 to 100. Scores above 70 strongly indicate bot behavior, while scores below 20 suggest human-like behavior patterns.",value_illustration,-1
137,fake,30,Cross-Platform Risk Index (CPRI),Evaluates risk across multiple platform types for the same account.,CPRI = SRS \times (1 + 0.2 \times \text{ipcountrynum}),calculation_knowledge,[4]
138,fake,31,Network Manipulation Index (NMI),Measures the extent of network manipulation considering bot behavior and coordination.,NMI = 0.6 \times BBI + 0.4 \times CAS,calculation_knowledge,"[3, 6]"
139,fake,32,Enhanced Trust Score (ETS),Calculates trust score considering both profile credibility and content authenticity.,ETS = 0.5 \times PCI + 0.5 \times CAS,calculation_knowledge,"[5, 1]"
140,fake,33,Coordinated Bot Risk (CBR),Assesses risk from coordinated bot networks.,CBR = BBI \times CAS \times \frac{\text{clustsize}}{100},calculation_knowledge,"[3, 6]"
141,fake,34,Content Security Index (CSI),Evaluates content security considering manipulation and authenticity.,CSI = 0.7 \times (1 - CMS) + 0.3 \times CAS,calculation_knowledge,"[8, 1]"
142,fake,35,Automated Behavior Score (ABS),Measures degree of automation in account behavior.,ABS = 0.4 \times BBI + 0.3 \times TEI + 0.3 \times (1 - CAS),calculation_knowledge,"[3, 7, 1]"
143,fake,36,Network Trust Score (NTS),Evaluates trustworthiness of account's network connections.,NTS = PCI \times (1 - NGV) \times (1 - CBR),calculation_knowledge,"[5, 2, 33]"
144,fake,37,Content Impact Score (CIS),Measures potential impact of manipulated content.,CIS = CMS \times MPS \times \frac{\text{netinflscore}}{100},calculation_knowledge,"[8, 9]"
145,fake,38,Authentication Risk Score (ARS),Assesses authentication-related risks.,ARS = 0.5 \times TEI + 0.3 \times (1 - PCI) + 0.2 \times SRS,calculation_knowledge,"[7, 5, 4]"
146,fake,39,Behavioral Anomaly Score (BAS),Quantifies unusual behavior patterns.,BAS = 0.4 \times BBI + 0.4 \times AAF + 0.2 \times NGV,calculation_knowledge,"[3, 0, 2]"
147,fake,40,High-Risk Bot Network,Identifies dangerous coordinated bot networks.,A Bot Network with CBR > 0.8 and SRS > 0.7,domain_knowledge,"[33, 4]"
148,fake,41,Trusted Content Creator,Identifies reliable content creators.,An account with ETS > 0.8 and CIS < 0.2,domain_knowledge,"[32, 37]"
149,fake,42,Authentication Risk Account,Identifies accounts with suspicious authentication patterns.,An account with ARS > 0.7 and at least one VPN Abuser detection,domain_knowledge,"[38, 16]"
150,fake,43,Network Security Threat,Identifies accounts posing network-level security risks.,An account with NTS < 0.3 and is part of a Bot Network,domain_knowledge,"[36, 11]"
151,fake,44,Content Manipulation Ring,Identifies coordinated content manipulation groups.,A Sockpuppet Network where all accounts have CMS > 0.7,domain_knowledge,"[14, 8]"
152,fake,45,Automated Spam Network,Identifies automated spam distribution networks.,A Bot Network where average ABS > 0.8 and all accounts are Content Farms,domain_knowledge,"[11, 35, 13]"
153,fake,46,Cross-Platform Threat,Identifies threats operating across multiple platforms.,A High-Risk Account with CPRI > 0.9 and is part of a Sockpuppet Network,domain_knowledge,"[10, 30, 14]"
154,fake,47,Behavioral Anomaly Cluster,Identifies groups showing unusual behavior patterns.,A cluster where average BAS > 0.8 and contains at least one Bot Network,domain_knowledge,"[39, 11]"
155,fake,48,Mass Manipulation Campaign,Identifies large-scale manipulation efforts.,A Content Manipulation Ring where CIS > 0.8 for all accounts,domain_knowledge,"[44, 37]"
156,fake,49,Advanced Persistent Threat,"Identifies sophisticated, persistent security threats.",A High-Risk Bot Network with NMI > 0.9 and TEI > 0.8,domain_knowledge,"[40, 31, 7]"
157,fake,50,Temporal Pattern Deviation Score (TPDS),Measures deviation from established temporal activity patterns.,TPDS = \sqrt{\sum_{i=1}^{24} (\frac{\text{obsfreq}_i - \text{expfreq}_i}{\text{expfreq}_i})^2},calculation_knowledge,"[0, 39]"
158,fake,51,Network Influence Centrality (NIC),Quantifies account's position and influence in interaction network.,NIC = 0.4 \times \text{connqualscore} + 0.3 \times \text{netinflscore} + 0.3 \times \text{interactdiv},calculation_knowledge,-1
159,fake,52,Multi-Account Correlation Index (MACI),Measures behavioral correlation across linked accounts.,"MACI = \frac{\sum_{i=1}^{n} \sum_{j=i+1}^{n} \text{corr}(i,j)}{\binom{n}{2}} where n is linked accounts",calculation_knowledge,"[35, 31]"
160,fake,53,Reputation Volatility Index (RVI),Quantifies stability of account reputation over time.,RVI = \frac{\sigma_{\text{reputscore}}}{\mu_{\text{reputscore}}} \times (1 + \frac{|\Delta\text{reputscore}|}{\Delta t}),calculation_knowledge,"[5, 4]"
161,fake,54,Content Distribution Pattern Score (CDPS),Analyzes patterns in content posting and sharing.,CDPS = 0.4 \times \text{entropy}(\text{posttimes}) + 0.3 \times \text{burstiness} + 0.3 \times (1 - \text{periodicity}),calculation_knowledge,"[8, 37]"
162,fake,55,Behavioral Consistency Score (BCS),Measures consistency of account behavior patterns.,BCS = (1 - TPDS) \times (1 - RVI) \times (1 - \frac{\text{patterndev}}{100}),calculation_knowledge,"[50, 53]"
163,fake,56,Network Synchronization Index (NSI),Quantifies synchronized activities across account clusters.,"NSI = \frac{\sum_{i=1}^{n} \sum_{j=i+1}^{n} \text{sync}(i,j)}{\text{clustsize}} \times MACI",calculation_knowledge,"[52, 31]"
164,fake,57,Content Amplification Effect (CAE),Measures the cascade effect of content sharing.,CAE = \text{CIS} \times NIC \times \log(1 + \text{resharecount}),calculation_knowledge,"[37, 51]"
165,fake,58,Authentication Pattern Score (APS),Evaluates consistency of authentication behaviors.,APS = (1 - TEI) \times BCS \times (1 - \frac{\text{authanom}}{100}),calculation_knowledge,"[7, 55]"
166,fake,59,Cross-Platform Correlation Score (CPCS),Measures behavioral correlation across platforms.,CPCS = CPRI \times MACI \times (1 + \frac{\text{platformlinks}}{10}),calculation_knowledge,"[30, 52]"
167,fake,60,Coordinated Influence Operation,Identifies sophisticated influence campaigns.,A network where NSI > 0.8 and CAE > 0.7 and contains at least one Content Manipulation Ring,domain_knowledge,"[56, 57, 44]"
168,fake,61,Behavioral Pattern Anomaly,Identifies accounts with inconsistent behavioral patterns.,An account with BCS < 0.3 and TPDS > 0.7 and is not a Trusted Account,domain_knowledge,"[55, 50, 12]"
169,fake,62,Cross-Platform Bot Network,Identifies coordinated bot activity across platforms.,A Bot Network where CPCS > 0.8 and all accounts have similar MACI patterns,domain_knowledge,"[11, 59, 52]"
170,fake,63,Authentication Anomaly Cluster,Identifies groups with suspicious authentication patterns.,A cluster where average APS < 0.3 and contains at least one Authentication Risk Account,domain_knowledge,"[58, 42]"
171,fake,64,Network Influence Hub,Identifies accounts with unusual influence patterns.,An account with NIC > 0.8 and CAE > 0.7 that is part of a Coordinated Influence Operation,domain_knowledge,"[51, 57, 60]"
172,fake,65,Reputation Manipulation Ring,Identifies coordinated reputation manipulation.,A Content Manipulation Ring where all accounts have RVI > 0.7 and similar CDPS patterns,domain_knowledge,"[44, 53, 54]"
173,fake,66,Synchronized Behavior Cluster,Identifies groups with highly synchronized activities.,A cluster where NSI > 0.9 and all accounts have similar BCS patterns,domain_knowledge,"[56, 55]"
174,fake,67,Multi-Platform Threat Network,Identifies sophisticated cross-platform threats.,A Cross-Platform Threat where CPCS > 0.8 and all accounts are part of a Synchronized Behavior Cluster,domain_knowledge,"[46, 59, 66]"
175,fake,68,Advanced Influence Campaign,Identifies sophisticated influence operations.,A Mass Manipulation Campaign containing at least one Network Influence Hub and high NSI,domain_knowledge,"[48, 64, 56]"
176,fake,69,Persistent Pattern Anomaly,Identifies sustained abnormal behavior patterns.,A Behavioral Pattern Anomaly that persists for over 30 days and maintains high TPDS,domain_knowledge,"[61, 50]"
177,fake,70,TEI quartile,Categorizes accounts into four groups based on their TEI values.,"Q_{TEI} = egin{cases} 1 & 	ext{if TEI} in [0, P_{25}] \ 2 & 	ext{if TEI} in (P_{25}, P_{50}] \ 3 & 	ext{if TEI} in (P_{50}, P_{75}] \ 4 & 	ext{if TEI} in (P_{75}, P_{100}] \end{cases} where P_n represents the nth percentile of TEI values",calculation_knowledge,[7]
178,fake,71,Latest Bot Likelihood Score (LBS),The most recent bot likelihood score for an account based on security detection timestamps.,LBS_a = \text{botlikscore}(\max_{t \in T_a} \text{detecttime}_t) where T_a is the set of all detection timestamps for account a,calculation_knowledge,[29]
179,fake,72,Reputational Risk,Measures the potential risk to an account’s reputation based on past moderation actions and low reputation scores.,"An account with reputscore < 30 and high abuserepnum, prioritized by the top quartile of abuse reports.",domain_knowledge,-1
180,fake,73,High-Impact Amplifier,"Identifies accounts with substantial network influence and frequent posting activity, acting as key amplifiers in coordinated networks.",An account with netinflscore > 80 and postfreq > 30 posts per day.,domain_knowledge,-1
181,fake,74,High-Activity Account,Identifies accounts with elevated engagement levels based on the number of sessions or total posting frequency.,An account with session_count > 1000 or total_post_frequency > 50.,domain_knowledge,-1
182,fake,75,Session Count (SC),Measures the total number of session records associated with an account.,"SC_a = \left| \{ \text{sessref} \in \text{sessionbehavior} \mid \text{sessprofref} = \text{profkey}, \text{profaccref} = a \} \right|",calculation_knowledge,-1
183,fake,76,Total Post Frequency (TPF),Measures the total posting frequency across all sessions for an account.,"TPF_a = \sum_{\text{cntref} \in \text{contentbehavior}} \text{postfreq}, \quad \text{where } \text{cntsessref} = \text{sessref}, \text{sessprofref} = \text{profkey}, \text{profaccref} = a",calculation_knowledge,-1
184,fake,77,High-Activity Account,Identifies accounts with elevated engagement levels based on the number of sessions or total posting frequency.,An account with session_count > 1000 or total_post_frequency > 50.,domain_knowledge,"[75, 76]"
185,fake,78,influence ranking by NIC,A ranking system that orders accounts based on their NIC scores from highest to lowest.,\text{rank}_i = |\{j : \text{NIC}_j > \text{NIC}_i\}| + 1 where i is the current account and j iterates over all accounts,calculation_knowledge,[51]
186,fake,79,TEI Risk Category,A categorical risk level assigned based on an account's TEI Quartile.,"A category assigned as 'Low Risk' (Quartile 1), 'Moderate Risk' (Quartile 2), 'High Risk' (Quartile 3), or 'Very High Risk' (Quartile 4) based on the account's calculated TEI Quartile.",domain_knowledge,[70]
187,fake,80,cluster identifier,A key used to group related accounts identified as part of the same network or cluster.,"In this context, the platform identifier (`platident`) associated with the accounts in the potential Amplification Network is the cluster identifier.",domain_knowledge,[19]
188,fake,81,member count,The total number of unique accounts within an identified cluster.,Calculated using COUNT(DISTINCT account_index) for accounts grouped by the cluster identifier.,calculation_knowledge,[80]
189,fake,82,maximum coordination score,The highest coordination score observed among the members of an identified cluster.,\max_{a \in cluster C} (\text{coordscore}_a),calculation_knowledge,[80]
190,fake,83,member account IDs,A collection (array) of the unique account indexes belonging to an identified cluster.,\{ \text{accindex}_a \mid a \in cluster C \},calculation_knowledge,[80]
191,fake,84,last activity proxy time,"An estimated timestamp of the last known activity for an account, used when direct session timestamps are unavailable or insufficient.",Derived as \max(\text{detecttime}) from associated securitydetection records for an account.,calculation_knowledge,-1
192,fake,85,review priority,A flag or status assigned to an account to indicate the need or priority level for manual review.,A field in the `account` table set to a specific value like 'Review_Inactive_Trusted' to signal that an otherwise trusted account requires review due to prolonged inactivity.,value_illustration,"[12, 84]"
193,fake,86,Account Inactivity,A condition indicating that an account has not demonstrated recent activity based on available data proxies.,Condition met when: \text{last activity proxy time} < (\text{CURRENT_DATE} - \text{'90 days'}),domain_knowledge,[84]
194,news,0,User Engagement Rate (UER),"Calculates the engagement rate of a user during a session by combining engagement score, page views, and session duration.","UER = \frac{\text{seshviews} \times \text{engscore}}{\text{seshdur}}, \text{ where seshviews is the number of articles viewed and seshdur is the duration in seconds.}",calculation_knowledge,-1
195,news,1,Article Quality Index (AQI),"Determines the quality index of an article by integrating quality score, freshness, sentiment, and controversy factors.","AQI = \frac{(qualscore + freshscore + sentscore - contrscore)}{3}, \text{ where each score is normalized on a uniform scale.}",calculation_knowledge,-1
196,news,2,Recommendation Relevance Score (RRS),"Computes the overall relevance of a recommendation by averaging recommendation score, algorithm confidence, and utility.","RRS = \frac{(recscore + confval + recutil)}{3}, \text{ where the contributing factors capture recommendation performance.}",calculation_knowledge,-1
197,news,3,Session Bounce Rate Adjustment (SBRA),Adjusts the raw session bounce rate by factoring in the click-through rate to better understand session quality.,"SBRA = bncrate \times \left(1 - \frac{ctrval}{100}\right), \text{ where bncrate is the bounce rate and ctrval is the click-through rate percentage.}",calculation_knowledge,-1
198,news,4,System Performance Index (SPI),"Measures overall system performance by considering response time, load, and performance scores.","SPI = \frac{(perfscore - loadscore) \times 100}{resptime}, \text{ where resptime is measured in milliseconds.}",calculation_knowledge,-1
199,news,5,Article Readability Score (ARS),Calculates the readability of an article by correlating estimated reading time with word count and difficulty factor.,"ARS = \frac{readsec \times \log(wordlen)}{w}, \text{ where w is a weight factor assigned according to the article difficulty level (e.g., 1 for Basic, 1.5 for Intermediate, 2 for Advanced, 1.2 for others).}",calculation_knowledge,-1
200,news,6,statusWeight,A weight assigned to different subscription tiers to reflect their business value.,"'Premium' = 2.0, 'Enterprise' = 3.0, 'Basic' = 1.0, and others = 0.5. It reflects the assumed revenue or importance of the subscription tier.",value_illustration,-1
201,news,7,genderFactor,A weighting value used to balance gender representation in demographic scoring.,"Assigned as 1.0 for Male, Female, and 0.8 otherwise. It standardizes the gender effect on demographic impact.",value_illustration,-1
202,news,8,occupationFactor,A multiplier that reflects how user occupation contributes to their demographic value.,"'Professional' = 1.5, 'Retired' = 1.2, 'Student' = 0.7, 'Other' = 1.0. These values are based on segmentation assumptions or business rules.",value_illustration,-1
203,news,9,Content Interaction Efficiency (CIE),Measures how efficiently a user interacts with content inside a session by looking at the average relative order in which events occur (their sequence values),"CIE = AVG(seqval)

Here `seqval` is the event’s sequence/position within a session (e.g., 1, 2, 3…).  Average it across all interactions in a session.",calculation_knowledge,-1
204,news,10,Premium Content Rule (PCR),Specifies that articles marked as Premium must meet higher quality and engagement benchmarks.,Premium articles are required to satisfy a defined quality threshold and demonstrate superior engagement metrics to enhance user satisfaction.,domain_knowledge,-1
205,news,11,Personalization Priority (PP),Establishes criteria for prioritizing content that aligns closely with individual user preferences based on prior interactions.,Content that strongly matches user preferences and exhibits high interaction rates is prioritized in personalized recommendations.,domain_knowledge,-1
206,news,12,AB Testing Cohort Analysis (ABTCA),Defines segmentation criteria for splitting users into cohorts for A/B testing experiments.,Users are assigned to predetermined groups to facilitate controlled comparisons of experimental feature performance across cohorts.,domain_knowledge,-1
207,news,13,Engagement Consistency Principle (ECP),States that consistent engagement across multiple sessions is a key indicator of long-term user loyalty.,"Users exhibiting steady engagement metrics over a series of sessions, irrespective of content type, are likely to display higher lifetime value.",domain_knowledge,[0]
208,news,14,Network Connection Quality Standard (NCQS),Defines the benchmark criteria for acceptable network performance to ensure reliable content delivery.,"A device connection meets optimal standards if it delivers network speed and connection quality above preset thresholds, facilitating smooth content consumption.",domain_knowledge,-1
209,news,15,Content Recommendation Strategy (CRS),Outlines the strategic framework used to select and rank recommended articles.,"Recommendation strategies are developed by jointly considering content relevance, user behavior analytics, and system performance metrics.",domain_knowledge,"[2, 4]"
210,news,16,User Behavior Paradigm (UBP),Describes typical patterns observed in user interactions within the platform.,"By analyzing metrics such as session duration, bounce rates, and interaction sequences, common behavioral patterns are identified which inform future personalization efforts.",domain_knowledge,"[0, 9]"
211,news,17,User Subscription Value (USV),Evaluates the relative value of a user's subscription based on subscription duration and status.,"USV = subdays \times statusWeight, \text{ where statusWeight is a multiplier defined by the subscription status (for example, Premium, Enterprise, or Basic).}",calculation_knowledge,[6]
212,news,18,Short Session Anomaly Detection (SSAD),Identifies sessions with unusually short durations that may signal disengagement or testing anomalies.,"Sessions with a duration significantly below the average, accompanied by low engagement and minimal page views, are flagged as anomalies.",domain_knowledge,[3]
213,news,19,Interaction Timeliness Indicator (ITI),Evaluates the promptness of user interactions after content presentation.,"A minimal delay between content exposure and user interaction indicates high interest, thereby enhancing content performance metrics.",domain_knowledge,[9]
214,news,20,Screen Resolution Illustration,Illustrates the significance of device screen resolution on overall user experience.,"Screen resolution values such as '1920x1080' indicate the clarity and detail of the display, impacting readability and visual appeal.",value_illustration,-1
215,news,21,Device OS Version Illustration,Depicts the relevance of the operating system version in assessing device performance and compatibility.,OS version details like 'Windows10.0.19042' provide insights into potential compatibility issues and performance benchmarks.,value_illustration,-1
216,news,22,Article Word Count Illustration,Demonstrates how the number of words in an article influences reading time and engagement.,"Higher word counts, such as 1200 words, generally correlate with longer estimated reading times and may affect reader retention.",value_illustration,-1
217,news,23,Network Speed Illustration,Shows the impact of network speed on the quality of content delivery.,"Network speeds measured in Mbps (for example, 25.75 Mbps) help assess potential buffering, latency issues, and overall user experience during content consumption.",value_illustration,-1
218,news,24,Accessibility Score Illustration,Explains how accessibility scores reflect the ease-of-use and inclusivity of content.,"An accessibility score like 90.00 demonstrates a high level of compliance with usability standards, ensuring that content is accessible to a wide range of users.",value_illustration,-1
219,news,25,App Version Quality Illustration,Assesses the importance of keeping app versions updated to improve interface quality and functionality.,Versions such as 'v3.1.2' suggest iterative improvements that enhance user interface performance and overall application stability.,value_illustration,-1
220,news,26,Geo-Location Data Illustration,Illustrates how geographical data aids in localizing content and understanding regional behavior patterns.,Geo-location indicators like 'San Francisco' or 'USA' provide context for regional content consumption and market-specific performance trends.,value_illustration,-1
221,news,27,Error Count Illustration,Demonstrates the importance of monitoring error counts to evaluate system reliability.,"Error counts, typically ranging from 0 to 5, serve as essential indicators of system health and operational robustness.",value_illustration,-1
222,news,28,Recommendation Position Illustration,Depicts the effect of recommendation placement on user visibility and engagement.,"A recommendation placed in position 3, for example, may benefit from higher prominence compared to lower-ranked placements.",value_illustration,-1
223,news,29,Content Format Variation Illustration,Highlights how different content formats influence user consumption and engagement.,"Variations such as 'Mobile', 'Text', 'HTML', or 'AMP' show how display adjustments cater to diverse user contexts and device capabilities.",value_illustration,-1
224,news,30,User Demographic Score (UDS),Measures the impact of user demographics by combining age with factors related to gender and occupation.,"UDS = ageval \times genderFactor \times occupationFactor, \text{ where genderFactor and occupationFactor are weights derived from segmentation studies.}",calculation_knowledge,"[7, 8]"
225,news,31,Real-Time Session Efficiency (RTSE),Evaluates session efficiency by balancing interaction outcomes against bounce adjustments.,"RTSE = \frac{CIE}{SBRA}, where CIE denotes the Content Interaction Efficiency and SBRA is the Session Bounce Rate Adjustment.",calculation_knowledge,"[3, 9]"
226,news,32,Dynamic Content Value (DCV),"Measures an article's overall value by combining quality, recommendation relevance, and readability factors.","DCV = \frac{AQI + RRS + (100 - ARS)}{3}, where AQI is the Article Quality Index, RRS the Recommendation Relevance Score, and ARS the Article Readability Score.",calculation_knowledge,"[1, 2, 5]"
227,news,33,Optimized Recommendation Score (ORS),Adjusts recommendation quality by incorporating system performance metrics.,"ORS = \frac{RRS \times SPI}{k}, where RRS reflects recommendation quality and SPI represents the System Performance Index, with k as a normalization constant.",calculation_knowledge,"[2, 4]"
228,news,34,Adjusted Read Time Estimator (ARTE),Provides an estimation of effective reading time by adjusting raw reading seconds.,"ARTE = readsec \times \frac{ARS}{UER}, where ARS is the Article Readability Score and UER is the User Engagement Rate.",calculation_knowledge,"[5, 0]"
229,news,35,Personalization Accuracy Metric (PAM),Computes the accuracy of personalization by balancing recommendation performance and priority assignment.,"PAM = \frac{RRS + PP}{2}, where RRS offers recommendation effectiveness and PP defines the Personalization Priority.",calculation_knowledge,"[2, 11]"
230,news,36,Conversion Impact Factor (CIF),Assesses the potential impact on conversions by linking engagement with prompt interaction.,"CIF = \frac{UER}{ITI}, where UER indicates user engagement and ITI measures the Interaction Timeliness Indicator.",calculation_knowledge,"[0, 19]"
231,news,37,Adjusted Bounce Ratio (ABR),Modifies the raw bounce metric by integrating real-time efficiency factors.,"ABR = SBRA \times (RTSE \; factor), with SBRA representing the Session Bounce Rate Adjustment and RTSE signifying Real-Time Session Efficiency.",calculation_knowledge,"[3, 31]"
232,news,38,Composite System Stability (CSS),Combines system and device performance to yield an overall stability measure.,"CSS = \sqrt{SPI \times DPM}, where SPI is the System Performance Index and DPM is the Device Performance Metric.",calculation_knowledge,"[4, 6]"
233,news,39,Interactive Content Amplifier (ICA),Enhances content value by factoring in user interactions and recommendation quality.,"ICA = CIE + (\alpha \times RRS), where CIE captures Content Interaction Efficiency and RRS contributes the Recommendation Relevance Score.",calculation_knowledge,"[9, 2]"
234,news,40,High Engagement Indicator (HEI),Determines if a session or article demonstrates outstanding user engagement.,An item is marked as high engagement when the User Engagement Rate is above the threshold and the Content Interaction Efficiency is high.,domain_knowledge,"[0, 9]"
235,news,41,Premium Article Distinction (PAD),Classifies articles as premium based on quality and content policy adherence.,An article qualifies as premium when it surpasses the Article Quality Index threshold and complies with the Premium Content Rule.,domain_knowledge,"[1, 10]"
236,news,42,Targeted Personalization Benchmark (TPB),Establishes a benchmark for assessing the effectiveness of personalized content.,Content meets the targeted personalization benchmark when it aligns with the criteria set by Personalization Priority and is corroborated by a high Personalization Accuracy Metric.,domain_knowledge,"[11, 35]"
237,news,43,User Churn Predictor (UCP),Identifies users at risk of churning through behavior pattern analysis.,Churn risk is flagged when inconsistent engagement per the Engagement Consistency Principle combines with anomalies detected by Short Session Anomaly Detection.,domain_knowledge,"[13, 18]"
238,news,44,Content Virality Threshold (CVT),Defines the performance level at which content is likely to go viral.,Content is considered viral when its Dynamic Content Value and Interactive Content Amplifier both exceed the established virality thresholds.,domain_knowledge,"[32, 39]"
239,news,45,System Resilience Factor (SRF),Assesses the robustness and fault tolerance of the system.,A system demonstrates resilience when the Composite System Stability and the System Performance Index both indicate strong performance.,domain_knowledge,"[38, 4]"
240,news,46,Session Drop-off Risk (SDR),Determines the likelihood of a session ending abruptly.,A session is at high drop-off risk when low Real-Time Session Efficiency coincides with an elevated Adjusted Bounce Ratio.,domain_knowledge,"[31, 37]"
241,news,47,Conversion Potential Indicator (CPI),Evaluates the potential of converting user interactions into conversions.,High conversion potential is indicated when the Conversion Impact Factor and the Interactive Content Amplifier are both optimized.,domain_knowledge,"[36, 39]"
242,news,48,Content Consumption Consistency (CCC),Indicates uniformity in user content consumption behavior.,"Consistent content consumption is achieved when the Article Readability Score, User Engagement Rate, and Personalization Priority together display steady patterns.",domain_knowledge,"[5, 0, 11]"
243,news,49,Subscription Valuation Rule (SVR),Determines the business value of a user by integrating subscription details with demographic and engagement factors.,"A comprehensive user valuation is achieved by weighting subscription duration, engagement performance, and demographic indicators.",domain_knowledge,"[17, 30]"
244,news,50,Composite User Activity Score (CUAS),Integrates multiple user activity metrics into one composite score.,"CUAS = \frac{UER + UDS + USV}{3}, where UER is the User Engagement Rate, UDS is the User Demographic Score, and USV is the User Subscription Value.",calculation_knowledge,"[0, 17, 30]"
245,news,51,Performance Status,"Categorizes system records based on response time into Critical, Warning, or Normal bands.","A CASE-based classification where resptime > 200 is 'Critical', >150 is 'Warning', and others are 'Normal'.",domain_knowledge,-1
246,news,52,Bounce Percentile,Ranks sessions by their adjusted bounce rate relative to all other sessions.,A percentile ranking computed using PERCENT_RANK() over all sessions ordered by adjusted_bounce_rate.,calculation_knowledge,[3]
247,news,53,CTR Percentile,Ranks sessions by their click-through rate relative to all other sessions.,A percentile ranking computed using PERCENT_RANK() over all sessions ordered by ctrval in descending order.,calculation_knowledge,-1
248,news,54,Performance Segment,Categorizes sessions based on their relative bounce rate and CTR performance using percentile rankings.,"A classification of sessions into performance categories based on specific percentile criteria: 'High Bounce, Low CTR' (bounce_percentile < 0.25, ctr_percentile < 0.25), 'High Bounce, High CTR' (bounce_percentile < 0.25, ctr_percentile >= 0.75), 'Low Bounce, Low CTR' (bounce_percentile >= 0.75, ctr_percentile < 0.25), 'Low Bounce, High CTR' (bounce_percentile >= 0.75, ctr_percentile >= 0.75), or 'Average Performance' for all other combinations.",domain_knowledge,"[52, 53]"
249,news,55,Cohort Percentage,Calculates the proportional representation of each test group within monthly registration cohorts,(Group registrations / Total monthly registrations) * 100,calculation_knowledge,[12]
250,news,56,Readability Weight Mapping,Defines difficulty-based weighting for readability calculations.,"Difficulty levels are assigned weights: Basic = 1, Intermediate = 1.5, Advanced = 2, Others = 1.2.",domain_knowledge,-1
251,news,57,Readability Segmentation,Classifies articles into readability categories based on calculated readability scores.,"Low readability: ARS < 50, Medium readability: 50 ≤ ARS ≤ 100, High readability: ARS > 100.",domain_knowledge,[5]
252,news,58,Recommendation Click-Through Rate (RCTR),Defines RCTR as the ratio of clicks to total recommendations.,RCTR = (clicks / recommendations) when recommendations are greater than zero.,calculation_knowledge,-1
253,news,59,Elite User Interaction Metric (EUIM),"A newly defined metric to identify elite user interactions by combining session clicks, views, and engagement score.","EUIM = (seshclicks + seshviews) * (engscore / 100), where a higher value indicates more elite or intense user interaction.",calculation_knowledge,-1
254,news,60,Interaction Default Values,A standardized set of initial values assigned to interaction behavior metrics when a new interaction record is created.,"A JSON structure: jsonb_build_object('scroll', jsonb_build_object('depth', 0, 'speed', 0.0, 'percentage', 0), 'exit_type', 'Natural', 'conversion', jsonb_build_object('value', 0, 'status', 'None'), 'time_spent', jsonb_build_object('viewport_time', 0, 'attention_time', 0, 'reading_seconds', 0, 'duration_seconds', 0), 'next_action', 'None', 'bounce_status', 'No', 'click_seconds', 0)",value_illustration,-1
255,alien,0,Signal-to-Noise Quality Indicator (SNQI),Combines SNR and noise floor to provide a unified signal quality metric.,"$\text{SNQI} = \text{SnrRatio} - 0.1 \times |\text{NoiseFloorDbm}|$, where higher values indicate better detection quality. Positive values generally indicate analyzable signals.",calculation_knowledge,-1
256,alien,1,Atmospheric Observability Index (AOI),Quantifies how conducive atmospheric conditions are for signal detection.,"$\text{AOI} = \text{AtmosTransparency} \times (1 - \frac{\text{HumidityRate}}{100}) \times (1 - 0.02 \times \text{WindSpeedMs})$, where values closer to 1 indicate ideal observation conditions.",calculation_knowledge,-1
257,alien,2,Signal Complexity Ratio (SCR),Measures the relationship between signal complexity and information density.,"$\text{SCR} = \frac{\text{ComplexIdx} \times \text{InfoDense}}{\log(\text{BwHz})}$, where higher values suggest potential artificial origin rather than natural phenomena.",calculation_knowledge,-1
258,alien,3,Technological Origin Likelihood Score (TOLS),Combines multiple factors to estimate likelihood of technological origin.,"$\text{TOLS} = \text{TechSigProb} \times (1 - \text{NatSrcProb}) \times \text{SigUnique} \times (0.5 + \frac{\text{AnomScore}}{10})$, where values above 0.75 warrant further investigation as potential technosignatures.",calculation_knowledge,-1
259,alien,4,Bandwidth-Frequency Ratio (BFR),"Measures the proportion of bandwidth to center frequency, helping identify signal type.","$\text{BFR} = \frac{\text{BwHz}}{\text{CenterFreqMhz} \times 10^6}$, where narrow ratios ($<0.001$) often indicate technological signals while wider ratios suggest natural phenomena.",calculation_knowledge,-1
260,alien,5,Detection Instrument Sensitivity Factor (DISF),Calculates the effective sensitivity of the detection setup based on telescope and environmental factors.,"$\text{DISF} = (10 - \frac{|\text{AirTempC} - 15|}{10}) \times \text{AtmosTransparency} \times (1 - \frac{\text{HumidityRate}}{200}) \times \frac{100 - \text{LunarDistDeg}}{100}$, where values closer to 10 indicate optimal detection sensitivity.",calculation_knowledge,-1
261,alien,6,Encoding Complexity Index (ECI),Evaluates the sophistication of potential encoding in the signal.,"$\text{ECI} = \frac{\text{CompressRatio} \times \text{ComplexIdx} \times \text{EntropyVal}}{10}$, where values above 1.5 suggest deliberate information encoding rather than random patterns.",calculation_knowledge,-1
262,alien,7,Signal Stability Metric (SSM),Quantifies overall temporal and spectral stability of a signal.,"$\text{SSM} = (1 - \frac{|\text{FreqDriftHzs}|}{\text{FreqMhz} \times 1000}) \times \frac{\text{SigDurSec}}{1 + \frac{\text{DoppShiftHz}}{1000}}$, where higher values indicate more stable signals typical of fixed transmitters.",calculation_knowledge,-1
263,alien,8,Research Priority Index (RPI),Helps researchers prioritize signals for follow-up based on multiple factors.,"$\text{RPI} = (\text{TechSigProb} \times 4 + \frac{\text{BioSigProb}}{100} + \text{SigUnique} \times 2 + \frac{\text{AnomScore}}{2}) \times (1 - \text{FalsePosProb})$, where values above 3 indicate high research priority.",calculation_knowledge,-1
264,alien,9,Lunar Interference Factor (LIF),Calculates the potential interference from lunar illumination on observations.,"$\text{LIF} = (1 - \frac{\text{LunarDistDeg}}{180}) \times (1 - \text{AtmosTransparency})$, where higher values indicate more lunar interference. Values above 0.5 suggest significant lunar contamination in data.",calculation_knowledge,-1
265,alien,10,Technosignature,Defines the concept of signals that indicate technological activity.,"A signal with $\text{TechSigProb} > 0.7$, $\text{NatSrcProb} < 0.3$, and $\text{ArtSrcProb} < 50$ that exhibits narrow bandwidth ($\text{BFR} < 0.001$) and high information density ($\text{InfoDense} > 0.8$).",domain_knowledge,[4]
266,alien,11,Coherent Information Pattern (CIP),Identifies signals showing patterns consistent with deliberate information transmission.,"Signals characterized by high signal stability ($\text{SSM} > 0.8$), organized information structure ($\text{EntropyVal}$ between 0.4-0.8), and consistent modulation ($\text{ModType}$ with $\text{ModIndex} > 0.5$).",domain_knowledge,[7]
267,alien,12,Target of Opportunity (TOO),Identifies high-value signals requiring immediate follow-up observation.,"Any signal with $\text{RPI} > 3.5$, $\text{TechSigProb} > 0.8$, and $\text{AnomScore} > 5$ that has not been previously documented or explained by known phenomena.",domain_knowledge,[8]
268,alien,13,Optimal Observing Window (OOW),Defines conditions when observational quality is maximized.,"Time periods when $\text{AOI} > 0.85$, $\text{LunarStage}$ is 'New' or 'First Quarter', $\text{LunarDistDeg} > 45$, and $\text{SolarStatus}$ is 'Low' or 'Moderate'.",domain_knowledge,"[1, 9]"
269,alien,14,Signal Degradation Scenario (SDS),Characterizes situations where signal quality is compromised by environmental factors.,"Observation conditions where one or more of: $\text{AtmosTransparency} < 0.7$, $\text{HumidityRate} > 70$, $\text{WindSpeedMs} > 8$, or $\text{GeomagStatus}$ contains 'Storm', resulting in compromised data quality.",domain_knowledge,[1]
270,alien,15,Narrowband Technological Marker (NTM),Identifies a specific signature associated with technological transmission.,"Signals with extremely narrow bandwidth ($\text{BFR} < 0.0001$), stable frequency ($\text{FreqDriftHzs} < 0.1$).",domain_knowledge,[4]
271,alien,16,Observational Confidence Level (OCL),Rates the reliability of observations based on conditions and equipment.,"A classification system with three tiers: 'High' ($\text{AOI} > 0.8$, $\text{EquipStatus} = \text{'Operational'}$, $\text{CalibrStatus} = \text{'Current'}$), 'Medium' ($\text{AOI}$ 0.5-0.8, minor equipment issues), and 'Low' ($\text{AOI} < 0.5$ or significant equipment problems).",domain_knowledge,[1]
272,alien,17,Potential Biosignature,Defines characteristics of signals potentially associated with biological processes.,"Signals with $\text{BioSigProb} > 0.6$, $\text{TechSigProb} < 0.4$, and spectral features that match known biological emission patterns, often associated with specific molecular transitions.",domain_knowledge,-1
273,alien,18,Encoded Information Transfer (EIT),Characterizes signals that appear to contain deliberate information encoding.,"Signals with $\text{ECI} > 1.8$, $\text{EntropyVal}$ between 0.3-0.7 (not random but structured), and consistent internal patterns that suggest language or data encoding schemes.",domain_knowledge,[6]
274,alien,19,Fast Radio Transient (FRT),"Defines a specific class of brief, high-energy radio emissions.","Signals with extremely short duration ($\text{SigDurSec} < 0.1$), high signal strength ($\text{SigStrDb} > 15$), broad bandwidth ($\text{BwHz} > 1000000$), and no periodicity ($\text{RepeatCount} = 1$).",domain_knowledge,-1
275,alien,20,WeathProfile: Clear,Illustrates optimal weather conditions for signal detection.,"Indicates pristine sky conditions with no clouds, usually associated with $\text{AtmosTransparency} > 0.9$, low $\text{HumidityRate} (< 40\%)$, and minimal $\text{WindSpeedMs} (< 3.0)$. Provides ideal visibility for optical observations and minimal atmospheric interference for radio observations.",value_illustration,-1
276,alien,21,SeeingProfile: Excellent,Illustrates superior atmospheric seeing conditions.,"Describes atmospheric conditions with minimal turbulence, allowing for sharp, detailed observations. Typically corresponds to image stability better than 1 arcsecond and is often associated with stable temperature gradients and low wind speeds ($\text{WindSpeedMs} < 2.5$).",value_illustration,-1
277,alien,22,SignalClass: Narrowband,Illustrates characteristics of narrowband signal detections.,Describes signals occupying a very narrow portion of the spectrum (typically $\text{BFR} < 0.0001$). Often associated with technological origins as natural sources rarely produce such spectrally confined emissions. These signals are particularly interesting in SETI research.,value_illustration,[4]
278,alien,23,GeomagStatus: Major Storm,Illustrates severe geomagnetic disturbance conditions.,"Indicates intense solar-induced geomagnetic activity with Kp index ≥ 7. During such conditions, ionospheric perturbations significantly affect radio observations below 100 MHz, aurora may be visible at mid-latitudes, and satellite communications may experience disruptions.",value_illustration,-1
279,alien,24,CIP Classification Label,Three-tier rating system for evaluating signal coherence against intelligent transmission criteria.,"Classification labels: 'Coherent Information Pattern Detected' ($\text{SSM} > 0.8$, $\text{EntropyVal}$ between 0.4-0.8, and $\text{ModIndex} > 0.5$), 'Potential Information Pattern' ($\text{SSM} > 0.6$ and $\text{EntropyVal}$ between 0.3-0.9$), or 'No Clear Pattern' (all other signals).",domain_knowledge,"[7, 11]"
280,alien,25,SigClassType: Broadband Transient,Illustrates a class of brief signals covering wide frequency ranges.,"Describes short-duration signals ($\text{SigDurSec}$ typically $< 5$) that span a large portion of the spectrum ($\text{BFR} > 0.1$). Examples include solar radio bursts, lightning discharges, and certain types of cosmic explosions like Fast Radio Bursts (FRBs).",value_illustration,[4]
281,alien,26,PolarMode: Circular,Illustrates circular polarization in detected signals.,Describes electromagnetic waves where the electric field vector rotates in a circular pattern as the wave propagates. Circular polarization maintaining high purity across frequency (indicated by $\text{PolarAngleDeg}$ stability) is rare in natural sources and may indicate technological origin.,value_illustration,-1
282,alien,27,EncryptEvid: Strong Pattern,Illustrates characteristics suggesting deliberate signal encoding.,"Indicates detection of non-random, internally consistent patterns that resist simple decoding but show hallmarks of designed encryption or encoding. Characterized by high $\text{EntropyVal} (> 0.7)$ combined with structural regularity that defies natural explanation.",value_illustration,-1
283,alien,28,EncodeType: Frequency Hopping,Illustrates a sophisticated encoding method used in telecommunications.,Describes a transmission technique where the signal rapidly switches frequencies according to a predetermined sequence. Detection would be characterized by discontinuous spectral features that follow a pattern. This technique is used on Earth to secure communications and reduce interference.,value_illustration,-1
284,alien,29,FalsePosProb: <0.01,Illustrates extremely high confidence in signal detection.,"Indicates less than 1% probability that the signal is a false detection or artifact. Such low false positive probability typically results from multiple independent confirmations, excellent signal strength (high $\text{SnrRatio}$), and elimination of all known terrestrial and instrumental sources.",value_illustration,-1
285,alien,30,Modulation Complexity Score (MCS),Quantifies the sophistication of signal modulation based on type and stability.,"$\text{MCS} = \text{ModIndex} \times (1 + \text{SSM}) \times M_{\text{factor}}$, where $M_{\text{factor}}$ is 2 for $\text{ModType} = \text{'AM'}$, 1.5 for 'FM', and 1 for other types. Incorporates Signal Stability Metric (SSM) to weight stable modulations higher.",calculation_knowledge,[7]
286,alien,31,Artificial Intelligence Detection Probability (AIDP),Calculates likelihood of artificial intelligence origin based on encoding complexity and technosignature indicators.,"$\text{AIDP} = \frac{\text{ECI} \times \text{TOLS}}{1 + \text{NatSrcProb}}$, where ECI (Encoding Complexity Index) and TOLS (Technological Origin Likelihood Score) are weighted against natural source probability.",calculation_knowledge,"[6, 3]"
287,alien,32,Observation Quality Factor (OQF),Provides a comprehensive measure of observational conditions quality.,"$\text{OQF} = \text{AOI} \times (1 - \text{LIF}) \times (\text{PointAccArc} < 2 ? 1 : \frac{2}{\text{PointAccArc}})$, where AOI (Atmospheric Observability Index) and LIF (Lunar Interference Factor) are combined with telescope pointing accuracy.",calculation_knowledge,"[1, 9]"
288,alien,33,Information Entropy Ratio (IER),Compares signal entropy to expected natural background entropy.,"$\text{IER} = \frac{\text{EntropyVal}}{\text{NatSrcProb} \times 0.9 + 0.1}$, where values significantly greater than 1 suggest non-natural information content. Uses NatSrcProb as a baseline for expected natural entropy.",calculation_knowledge,-1
289,alien,34,Signal Processing Efficiency Index (SPEI),Evaluates the computational efficiency of signal processing relative to complexity.,"$\text{SPEI} = \frac{\text{DecodeIters} \times \text{ProcTimeHrs}}{\text{ECI} \times \text{ComplexIdx}}$, where ECI (Encoding Complexity Index) provides the complexity component to normalize processing time and iterations.",calculation_knowledge,[6]
290,alien,35,Celestial Location Significance Factor (CLSF),Calculates significance of signal source location based on astronomical targets of interest.,"$\text{CLSF} = (\text{CelestObj} ? 2 : 1) \times (\text{ObjType} == \text{'Giant'} \&\& \text{ObjMassSol} \text{ between } 0.8 \text{ and } 1.2 ? 1.5 : 1) \times (\text{ObjMetal} > 0 ? \text{ObjMetal} + 1 : 0.5)$, where higher values indicate source locations more likely to harbor intelligent life.",calculation_knowledge,-1
291,alien,36,Confirmation Confidence Score (CCS),Quantifies overall confidence in signal verification across multiple parameters.,"$\text{CCS} = (1 - \text{FalsePosProb}) \times \text{DecodeConf} \times \text{ClassConf} \times (\text{SNQI} > 0 ? \frac{\text{SNQI}}{10} + 0.5 : 0.1)$, where SNQI (Signal-to-Noise Quality Indicator) provides a quality weighting factor.",calculation_knowledge,[0]
292,alien,37,Habitable Zone Signal Relevance (HZSR),Assesses signal relevance based on source's position in habitable zone.,"$\text{HZSR} = \text{TOLS} \times (\text{ObjType} == \text{'Dwarf'} ? (0.7 \leq \text{ObjMassSol} \leq 1.4 ? (0.8 \leq \frac{\text{SourceDistLy}}{\sqrt{\text{ObjMassSol}}} \leq 1.7 ? 2 : 0.5) : 0.3) : 0.1)$, where TOLS (Technological Origin Likelihood Score) is weighted by stellar habitability factors.",calculation_knowledge,[3]
293,alien,38,Pattern Recognition Confidence (PRC),Measures confidence in identified signal patterns based on multiple factors.,"$\text{PRC} = (\text{RepeatCount} > 1 ? 1 + \log_{10}(\text{RepeatCount}) : 0.5) \times (\text{EntropyVal} < 0.9 ? 1 : 0.3) \times \text{SCR}$, where SCR (Signal Complexity Ratio) provides complexity weighting.",calculation_knowledge,[2]
294,alien,39,NTM Classification System,A tiered classification system for Narrowband Technological Markers based on signal characteristics.,"Three-tier classification: 'Strong NTM' (BFR < 0.0001 AND FreqDriftHzs < 0.1 AND non-natural modulation), 'Moderate NTM' (BFR < 0.0005 AND FreqDriftHzs < 0.5 AND non-natural modulation), and 'Not NTM' (all other signals).",domain_knowledge,"[15, 4]"
295,alien,40,High-Confidence Technosignature,Defines signals with extremely high likelihood of technological origin.,"A Technosignature with $\text{CCS} > 0.9$, $\text{MCS} > 1.5$, and $\text{AIDP} > 0.8$, indicating a signal that meets the basic Technosignature criteria with additional confirmation through modulation complexity and artificial intelligence detection markers.",domain_knowledge,"[10, 30, 31, 36]"
296,alien,41,Habitable Zone Transmission,Identifies signals originating from stellar habitable zones with technological characteristics.,"A signal with $\text{HZSR} > 1.5$ and Technosignature characteristics, originating from a star system with conditions potentially suitable for life, making it a priority candidate for SETI research.",domain_knowledge,"[10, 37]"
297,alien,42,Multi-Channel Communication Protocol,Identifies signal patterns consistent with sophisticated communication protocols.,"Signal exhibiting Coherent Information Pattern (CIP) characteristics across multiple frequency channels with coordinated timing ($\text{RepeatCount} > 3$, $\text{PeriodSec}$ consistent across observations) and $\text{ECI} > 2.0$, suggesting a designed communication system.",domain_knowledge,"[11, 6]"
298,alien,43,Quantum-Coherent Transmission,Describes signals potentially employing quantum properties for communication.,"Signals with $\text{QuantEffects}$ containing 'Significant' or 'Observed' patterns, exhibiting unusually high information density ($\text{InfoDense} > 1.5$) while maintaining an $\text{ECI} > 2.5$, suggesting advanced transmission technologies beyond conventional radiofrequency methods.",domain_knowledge,[6]
299,alien,44,Research Critical Signal,Defines signals requiring immediate and extensive scientific resources.,"Signals meeting Target of Opportunity (TOO) criteria with additional $\text{PRC} > 0.8$ and $\text{IMDF} < 0.5$, indicating high-quality, minimally distorted signals that show recognizable patterns warranting priority allocation of research resources.",domain_knowledge,"[12, 38, 39]"
300,alien,45,Directed Transmission,Identifies signals that appear specifically directed rather than omnidirectional.,"Signals with high spatial stability ($\text{SpatStab} = \text{'Moderate'}$), narrow beam characteristics ($\text{PolarMode} = \text{'Linear'}$ with stable $\text{PolarAngleDeg}$), and high $\text{TOLS} > 0.85$, suggesting intentional transmission toward our location.",domain_knowledge,[3]
301,alien,46,Signal of Galactic Significance,Classifies signals with potential importance to galactic civilization models.,"Signals originating from regions of high $\text{CLSF} (> 2.0)$ that display Technosignature characteristics and have $\text{AIDP} > 0.7$, representing potential evidence of advanced civilizations at galactic-relevant locations.",domain_knowledge,"[10, 31, 35]"
302,alien,47,CCS Approximation,Simplified CCS calculation using direct signal-to-noise ratio values when full Signal-to-Noise Quality Indicator (SNQI) data is unavailable.,$(1 - \text{FalsePosProb}) \times \text{DecodeConf} \times (\text{SNR} - 0.1 \times |\text{NoiseFloorDbm}| > 0 ? \frac{\text{SNR} - 0.1 \times |\text{NoiseFloorDbm}|}{10} + 0.5 : 0.1)$,calculation_knowledge,[36]
303,alien,48,Observation-Verified Signal,Defines signals that have undergone rigorous verification processes.,"Signals observed under Optimal Observing Window (OOW) conditions with $\text{OQF} > 0.85$ and $\text{CCS} > 0.8$, indicating high-quality observations with multiple verification methods applied.",domain_knowledge,"[13, 32, 36]"
304,alien,49,Anomalous Quantum Signal,Describes signals exhibiting quantum properties inconsistent with current physics models.,"Signals with $\text{QuantEffects}$ indicating anomalous behavior, $\text{AnomScore} > 8$, and unusually high $\text{MCS} (> 2.0)$, suggesting either unknown natural quantum phenomena or extremely advanced transmission technologies beyond current human capabilities.",domain_knowledge,[30]
305,alien,50,Analyzable Signals,Signals of sufficient quality to be considered useful for further analysis.,Signals with SNQI > 0 are considered analyzable.,domain_knowledge,[0]
306,alien,51,Bandwidth-to-Frequency Ratio (BFR),Normalized signal width relative to its central frequency.,"$\text{BFR} = \frac{\text{BwHz}}{\text{CenterFreqMhz} \times 1{,}000{,}000}$, used to characterize signal spread relative to its frequency band.",calculation_knowledge,-1
307,alien,52,TOLS Category,Classification of signals based on TOLS thresholds.,"Categorized as 'Low' if TOLS < 0.25, 'Medium' if TOLS < 0.75, and 'High' otherwise.",domain_knowledge,[3]
308,alien,53,High Lunar Interference Events,Observations with significant lunar interference.,"Events where the calculated LIF is greater than 0.5, indicating strong lunar contamination in the data.",domain_knowledge,[9]
309,alien,54,High Confidence Signals,"Signal with Confirmation Confidence Score (CCS) > 0.8, indicating high reliability.",Signals where $\text{CCS} > 0.8$,domain_knowledge,[36]
310,alien,55,Equipment Problems,Defines what counts as an abnormal condition for a telescope’s subsystems.,A telescope is considered to have an equipment problem whenever **any** of its key subsystem states are not in their nominal condition: • Equipment status is not “Operational”; • Calibration status is not “Current”; • Cooling-system status is not “Normal”.,domain_knowledge,-1
311,museum,0,Conservation Priority Index (CPI),Calculates the overall conservation priority for an artifact based on multiple factors.,"CPI = \frac{(HistSignRating + ResearchValRating + CultScore) \times (10 - ConserveStatus)}{30}, \text{where ConserveStatus is numerically mapped: Excellent=1, Good=3, Fair=5, Poor=7, Critical=10}",calculation_knowledge,-1
312,museum,1,Sensitivity Weight Values,Numerical weights for sensitivity calculations,"EnvSensitivity: Low=1, Medium=5, High=10; LightSensitivity: Low=1, Medium=5, High=10; TempSensitivity: Low=1, Medium=5, High=10; HumiditySensitivity: Low=1, Medium=5, High=10",value_illustration,-1
313,museum,2,Environmental Risk Factor (ERF),Quantifies the overall environmental risk to an artifact based on its sensitivities.,"ERF = \frac{\sum_{i \in sensitivities} SensWeight_i}{|sensitivities|}, \text{where sensitivities includes EnvSensitivity, LightSensitivity, TempSensitivity, etc, with value mapping based on Sensitivity Weight Values.}",calculation_knowledge,[1]
314,museum,3,Artifact Vulnerability Score (AVS),Comprehensive score indicating how vulnerable an artifact is based on its conservation priority and environmental sensitivities.,"AVS = CPI \times ERF, \text{where higher scores indicate artifacts requiring more urgent attention}",calculation_knowledge,"[0, 2]"
315,museum,4,Display Safety Duration (DSD),Calculates the recommended maximum display duration for an artifact based on its sensitivities.,"DSD = \frac{BaseDuration \times (10 - LightSensWeight) \times (10 - TempSensWeight) \times (10 - HumidSensWeight)}{1000}, \text{where BaseDuration=36 months, and SensWeight uses Sensitivity Weight Values.}",calculation_knowledge,[1]
316,museum,5,Showcase Environmental Stability Rating (SESR),Measures how well a showcase maintains stable environmental conditions.,"SESR = 10 - \frac{(TempVar24h + \frac{HumVar24h}{5} + LeakRate)}{3}, \text{where higher scores indicate more stable showcases}",calculation_knowledge,-1
317,museum,6,Artifact Exhibition Compatibility (AEC),Determines how compatible an artifact is with its current showcase environment.,"AEC = 10 - |ERF - SESR|, \text{where a score closer to 10 indicates better compatibility}",calculation_knowledge,"[2, 5]"
318,museum,7,Material Deterioration Rate (MDR),Estimates the rate of material deterioration based on environmental factors.,"MDR = \frac{ArtAgeYears \times ERF \times (RelHumidity - 50)^2 \times TempC}{100000}, \text{where higher values indicate faster deterioration}",calculation_knowledge,[2]
319,museum,8,Light Exposure Risk (LER),Quantifies the risk from light exposure based on artifact sensitivity and current light levels.,"LER = \frac{LightLux \times LightSensWeight \times VisibleExpLxh}{1000}, \text{where LightSensWeight uses Sensitivity Weight Values}",calculation_knowledge,[1]
320,museum,9,Conservation Budget Efficiency (CBE),Measures the efficiency of conservation budget allocation relative to artifact importance.,"CBE = \frac{\sum_{i \in artifacts} (CPI_i \times BudgetRatio_i)}{|artifacts|}, \text{where BudgetRatio is the proportion of total conservation budget allocated to each artifact}",calculation_knowledge,[0]
321,museum,10,Visitor Impact Risk (VIR),Assesses the risk posed by visitor traffic to artifacts in exhibition halls.,"VIR = \frac{VisitorCountDaily \times VisitorFlowRate \times VisitorDwellMin}{1000}, \text{where VisitorFlowRate is numerically mapped: Low=1, Medium=3, High=5}",calculation_knowledge,-1
322,museum,11,High-Value Artifact,"Identifies artifacts with exceptional historical, cultural, or monetary value requiring special attention.","An artifact is considered high-value when its InsValueUSD exceeds $1,000,000 OR when both HistSignRating and CultScore are in the top 10% of all artifacts.",domain_knowledge,-1
323,museum,12,Conservation Emergency,Identifies artifacts requiring immediate conservation intervention.,A situation where an artifact has ConserveStatus='Critical' AND a TreatPriority='Urgent'.,domain_knowledge,-1
324,museum,13,Environmental Instability Event,Identifies periods when showcase environmental conditions fluctuate beyond acceptable parameters.,Occurs when TempVar24h > 1°C OR HumVar24h > 3 within a 24-hour period.,domain_knowledge,-1
325,museum,14,Accelerated Deterioration Scenario,Identifies conditions that could lead to rapid artifact deterioration.,Occurs when MDR > 5 AND at least two SensitivityData values are 'High'.,domain_knowledge,[7]
326,museum,15,Exhibition Rotation Candidate,Identifies artifacts that should be considered for rotation out of display.,An artifact is a rotation candidate when its current display duration exceeds 75% of its DSD OR LER > 7.,domain_knowledge,"[4, 8]"
327,museum,16,Showcase Failure Risk,Identifies showcases at risk of failing to maintain proper environmental conditions.,"Occurs when SESR < 4 OR at least three of the following are true: SealCondition='Poor', MaintStatus='Overdue', FilterStatus='Replace Now', SilicaGelStatus='Replace Now'.",domain_knowledge,[5]
328,museum,17,Conservation Budget Crisis,Identifies when conservation budget allocation is insufficient for high-priority artifacts.,Occurs when CBE < 0.5 AND at least one artifact has ConserveStatus='Critical' and BudgetAllocStatus='Insufficient'.,domain_knowledge,[9]
329,museum,18,Dynasty Value Artifact,Identifies artifacts from historically significant dynasties with higher research and cultural value.,"Artifacts from 'Ming', 'Han', or 'Tang' dynasties that also have ResearchValRating > 8.",domain_knowledge,-1
330,museum,19,Visitor Crowd Risk,Identifies exhibition halls where high visitor numbers pose risks to artifact safety.,Occurs when VIR > 5 AND SecLevel='Level 1' for any artifact in the hall.,domain_knowledge,[10]
331,museum,20,Organic Material Vulnerability,Identifies organic materials requiring special environmental conditions.,"Artifacts with MatType='Wood', 'Textile', or 'Paper' AND EnvSensitivity='High' require specialized environmental controls with narrower humidity and temperature ranges than inorganic materials.",domain_knowledge,-1
332,museum,21,ArtifactsCore.ConserveStatus,Illustrates the conservation status values and their meanings.,"Values range from 'Excellent' (recently conserved, no issues), 'Good' (stable with minor issues), 'Fair' (stable but with noticeable issues), 'Poor' (active deterioration), to 'Critical' (severe deterioration requiring immediate intervention).",value_illustration,-1
333,museum,22,ArtifactRatings.HistSignRating,Illustrates the historical significance rating scale.,"A SMALLINT typically ranging from 1-10, where 1-3 indicates minor historical significance, 4-7 indicates moderate significance, and 8-10 indicates exceptional historical importance that fundamentally contributes to our understanding of past cultures or events.",value_illustration,-1
334,museum,23,SensitivityData.LightSensitivity,Illustrates light sensitivity classifications and their implications.,"Values include 'Low' (can tolerate up to 300 lux, like stone artifacts), 'Medium' (should be limited to 150-200 lux, like oil paintings), and 'High' (restricted to 50 lux or less, like textiles and works on paper).",value_illustration,-1
335,museum,24,SensitivityData.HumiditySensitivity,Illustrates humidity sensitivity classifications and their implications.,"Values include 'Low' (can tolerate 30-65% RH, like stone artifacts), 'Medium' (requires 40-60% RH, like wood), and 'High' (requires 45-55% RH with minimal fluctuation, like lacquer work).",value_illustration,-1
336,museum,25,ExhibitionHalls.CCTVCoverage,Illustrates CCTV coverage classifications.,"'Full' indicates 100% exhibition space coverage with overlapping cameras, 'Partial' indicates 60-90% coverage with possible blind spots, and 'Limited' indicates less than 60% coverage focusing only on high-value areas.",value_illustration,-1
337,museum,26,Showcases.Airtightness,Illustrates showcase airtightness measurement.,"A REAL value typically ranging from 0.01 (extremely airtight, less than 0.01 air changes per day) to 5.0 (poor airtightness, multiple air changes per day). Museum-grade showcases typically maintain values below 0.1.",value_illustration,-1
338,museum,27,EnvironmentalReadingsCore.TempC,Illustrates temperature readings and their conservation implications.,"Values typically range from 15-25°C, with 18-22°C being ideal for most collections. Fluctuations greater than 2°C within 24 hours can stress materials. Values outside 10-30°C indicate environmental control failure requiring immediate attention.",value_illustration,-1
339,museum,28,EnvironmentalReadingsCore.RelHumidity,Illustrates relative humidity readings and their conservation implications.,"Values typically range from 30-65%, with 45-55% being ideal for mixed collections. Fluctuations greater than 5% within 24 hours can cause dimensional changes in organic materials. Values below 30% risk embrittlement, while above 65% risk mold growth.",value_illustration,-1
340,museum,29,LightAndRadiationReadings.LightLux,Illustrates light intensity measurements and their conservation implications.,"Values in lux range from near-dark (5-10 lux) to typical indoor lighting (300-500 lux). Conservation standards recommend 50 lux for highly sensitive materials, 150-200 lux for paintings and wood, and up to 300 lux for stone and metal. Daylight can exceed 10,000 lux and should be filtered.",value_illustration,-1
341,museum,30,AirQualityReadings.PM25Conc,Illustrates fine particulate matter measurements and their conservation implications.,"Values represent PM2.5 concentration in µg/m³. Clean museum air should measure below 5 µg/m³. Values of 5-15 indicate acceptable conditions, 15-30 indicate potential risk to sensitive materials, and above 30 represent hazardous conditions requiring immediate air filtration review.",value_illustration,-1
342,museum,31,Total Environmental Threat Level (TETL),Comprehensive measurement of all environmental threats to an artifact based on multiple risk factors.,"TETL = ERF + LER + (MDR × 2), where ERF is the Environmental Risk Factor, LER is the Light Exposure Risk, and MDR is the Material Deterioration Rat.",calculation_knowledge,"[2, 8, 7]"
343,museum,32,Showcase Protection Adequacy (SPA),Measures how well a showcase protects its artifacts based on its stability and the artifacts' requirements.,"SPA = SESR - (ERF × 0.5), where SESR is the Showcase Environmental Stability Rating and ERF is the Environmental Risk Factor. Positive values indicate adequate protection.",calculation_knowledge,"[5, 2]"
344,museum,33,Conservation Backlog Risk (CBR),Quantifies the risk associated with delayed conservation treatments.,"CBR = (CPI × (Days since LastCleaningDate - CleanIntervalDays)) ÷ 100, where CPI is the Conservation Priority Index. Higher values indicate higher risk from delayed conservation.",calculation_knowledge,[0]
345,museum,34,Visitor Capacity Safety Factor (VCSF),Determines the safe visitor capacity for exhibition halls containing sensitive artifacts.,"VCSF = VIR ÷ (AVS × 0.1), where VIR is the Visitor Impact Risk and AVS is the Artifact Vulnerability Score. Lower values indicate safer visitor capacities.",calculation_knowledge,"[10, 3]"
346,museum,35,Exhibition Safety Quotient (ESQ),"Comprehensive safety rating for an exhibition based on artifacts, showcases, and visitor factors.","ESQ = ((10 - AVS) + AEC + (10 - VIR)) ÷ 3, where AVS is the Artifact Vulnerability Score, AEC is the Artifact Exhibition Compatibility, and VIR is the Visitor Impact Risk. Higher values indicate safer exhibitions.",calculation_knowledge,"[3, 6, 10]"
347,museum,36,Conservation Resource Allocation Efficiency (CRAE),Measures how efficiently conservation resources are allocated based on priorities and budget.,"CRAE = CBE × (1 - (CBR ÷ 10)), where CBE is the Conservation Budget Efficiency and CBR is the Conservation Backlog Risk. Higher values indicate more efficient resource allocation.",calculation_knowledge,"[9, 33]"
348,museum,37,Material Aging Projection (MAP),Projects the rate of artifact aging based on material type and environmental conditions.,"MAP = MDR × (1 + (TETL ÷ 20)), where MDR is the Material Deterioration Rate and TETL is the Total Environmental Threat Level. Higher values indicate faster projected aging.",calculation_knowledge,"[7, 31]"
349,museum,38,Exhibition Rotation Priority Score (ERPS),Calculates priority for rotating artifacts in and out of exhibition based on multiple factors.,"ERPS = (DSD - DisplayDurMonths) × (LER + 1) × (CPI + 1) ÷ 100, where DSD is the Display Safety Duration, LER is the Light Exposure Risk, and CPI is the Conservation Priority Index. Lower values indicate higher rotation priority.",calculation_knowledge,"[4, 8, 0]"
350,museum,39,Environmental Compliance Index (ECI),Measures how well current environmental conditions meet the requirements for an artifact.,"ECI = 10 - (|(TempC - IdealTemp)| + |(RelHumidity - IdealHumidity)| ÷ 5 + ERF ÷ 2), where ERF is the Environmental Risk Factor. Higher values indicate better compliance with requirements.",calculation_knowledge,[2]
351,museum,40,Security Risk Exposure (SRE),Quantifies an artifact's exposure to security risks based on value and security measures.,"SRE = (InsValueUSD ÷ 100000) × (10 - VIR) ÷ 10, where VIR is the Visitor Impact Risk. Higher values indicate greater security risk exposure.",calculation_knowledge,[10]
352,museum,41,Critical Conservation Alert,Identifies artifacts in critical condition requiring immediate intervention.,An artifact that meets both the Conservation Emergency criteria AND has an AVS > 8.,domain_knowledge,"[12, 3]"
353,museum,42,High Deterioration Risk Artifact,Identifies artifacts at high risk of rapid deterioration due to environmental factors.,An artifact that has TETL > 15 AND falls under the Accelerated Deterioration Scenario.,domain_knowledge,"[31, 14]"
354,museum,43,Exhibition Rotation Urgency,Identifies artifacts that should be immediately removed from exhibition.,Occurs when an artifact is an Exhibition Rotation Candidate AND has an ERPS < 0.,domain_knowledge,"[15, 38]"
355,museum,44,Showcase Compatibility Issue,Identifies incompatible artifact-showcase pairings requiring adjustment.,Occurs when SPA < 0 AND the artifact is classified as having High-Value.,domain_knowledge,"[32, 11]"
356,museum,45,Conservation Resource Crisis,Identifies serious conservation resource allocation problems.,Occurs when CRAE < 0.3 AND there is a Conservation Budget Crisis.,domain_knowledge,"[36, 17]"
357,museum,46,Dynasty Artifact at Risk,Identifies historically significant dynasty artifacts at conservation risk.,An artifact that qualifies as a Dynasty Value Artifact AND has a MAP > 3.,domain_knowledge,"[18, 37]"
358,museum,47,Environmental Control Failure,Identifies situations where environmental controls are failing to protect artifacts.,Occurs when ECI < 4 AND there is an Environmental Instability Event.,domain_knowledge,"[39, 13]"
359,museum,48,High Security Priority Artifact,Identifies artifacts requiring enhanced security measures.,An artifact that is classified as High-Value AND has an SRE > 5.,domain_knowledge,"[11, 40]"
360,museum,49,Visitor Traffic Safety Concern,Identifies situations where visitor traffic poses safety concerns for exhibitions.,Occurs when VCSF > 2 AND there is a Visitor Crowd Risk situation.,domain_knowledge,"[34, 19]"
361,museum,50,Organic Material Emergency,Identifies emergency situations for organic materials.,Occurs when an artifact falls under the Organic Material Vulnerability classification AND has a TETL > 12.,domain_knowledge,"[20, 31]"
362,museum,51,High-Value Category,Classification system for categorizing high-value artifacts based on their monetary or cultural/historical significance.,"An artifact falls into 'Monetary High-Value' category when its InsValueUSD exceeds $1,000,000. It qualifies as 'Cultural/Historical High-Value' when both its HistSignRating and CultScore are in the top 10% of all artifacts (percentile rank = 1). Otherwise 'Other'.",domain_knowledge,[11]
363,museum,52,ERPS Decision Threshold,Converts ERPS scores into conservation actions,"When ERPS < 0, trigger 'Immediate Rotation'; otherwise 'Monitor'.",domain_knowledge,[38]
364,museum,53,Light Exposure Thresholds,Defines maximum safe light exposure levels for artifacts based on material sensitivity,"High sensitivity artifacts (textiles, paper) must not exceed 50 lux; Medium sensitivity (paintings, wood) must not exceed 200 lux. Based on conservation research about light-induced deterioration rates.",domain_knowledge,"[8, 21]"
365,museum,54,Conservation Environment Chronology (CEC),"A methodological approach used in museum conservation to segment environmental monitoring data into chronological intervals. This approach enables curators to identify seasonal trends, long-term shifts, and anomalies in environmental conditions that can affect artifact stability and preservation.","CEC involves grouping readings (e.g., temperature, humidity, air pressure) by fixed time intervals (such as by year) to discern patterns and inform conservation strategies.",domain_knowledge,-1
366,museum,55,Artifact Rarity & Valuation (ARV),Establishes criteria for identifying artifacts of exceptional rarity and valuation that demand heightened preservation measures and limited public access.,"Artifacts are categorized as ARV if their insurance value exceed $1,000,000.",domain_knowledge,-1
367,museum,56,Conservation Priority Level,Classifies artifacts into priority levels based on their CPI scores,'High Priority': CPI > 7 (Artifacts requiring immediate conservation attention); 'Medium Priority': 4 < CPI ≤ 7 (Artifacts needing monitoring and planned conservation); 'Low Priority': CPI ≤ 4 (Artifacts in stable condition).,domain_knowledge,[0]
368,disaster,0,hazlevel,Illustrates the severity classification of disaster events,"Level 1 represents minimal threat, Level 2 indicates moderate danger, Level 3 shows significant hazard, Level 4 denotes severe emergency situation, and Level 5 signifies catastrophic conditions requiring maximum response",value_illustration,-1
369,disaster,1,emerglevel,Illustrates the color-coded emergency classification system,"Yellow indicates monitoring phase with minimal activation, Orange represents partial activation with elevated alert, Red signifies full activation for serious emergencies, and Black denotes critical emergency situations requiring all available resources",value_illustration,-1
370,disaster,2,respphase,Illustrates the progression of disaster response operations,"Initial phase focuses on immediate life-saving actions, Emergency phase addresses critical needs and stabilization, Recovery phase works on restoring basic services and infrastructure, and Reconstruction phase involves long-term rebuilding efforts",value_illustration,-1
371,disaster,3,lastmilestatus,Illustrates the state of final delivery operations to affected populations,"On Track indicates deliveries proceeding as scheduled, Delayed shows deliveries facing non-critical setbacks, and Suspended means deliveries have temporarily halted due to severe constraints",value_illustration,-1
372,disaster,4,fundingstate,Illustrates the financial resource condition for operations,"Adequate means sufficient funds available for current operations, Limited indicates funding constraints affecting some activities, and Critical represents severe funding shortages threatening essential operations",value_illustration,-1
373,disaster,5,impactMetrics.communication,Illustrates the condition of communication infrastructure,"Operational indicates fully functioning communication networks, Limited means partial communication capabilities with restrictions, and Down represents complete failure of communication infrastructure requiring alternative methods",value_illustration,-1
374,disaster,6,resourceallocstate,Illustrates the adequacy of resource distribution,"Sufficient means resources meet current operational needs, Limited indicates resources are stretched and prioritization is necessary, and Critical represents severe shortages requiring immediate supplementation",value_illustration,-1
375,disaster,7,impactMetrics.damage_level,Illustrates the extent of destruction caused by disasters,"Minor represents limited structural damage with most systems functional, Moderate indicates significant damage with some systems compromised, Severe shows extensive damage with most systems affected, and Catastrophic represents complete devastation with total system failures",value_illustration,-1
376,disaster,8,staffingProfile.readiness.ppe_status,Illustrates the availability of Personal Protective Equipment,"Adequate means sufficient PPE available for all personnel, Limited indicates restrictions in PPE distribution requiring prioritization, and Critical represents severe shortages threatening staff safety and operational continuity",value_illustration,-1
377,disaster,9,coordeffectlvl,Illustrates the quality of coordination between responding agencies,"High indicates seamless integration and information sharing across agencies, Medium represents adequate coordination with occasional communication gaps, and Low indicates significant coordination problems hindering effective response",value_illustration,-1
378,disaster,10,Resource Utilization Ratio (RUR),Measures how effectively hub capacity is being used relative to available resources,RUR = \frac{hubutilpct}{100} \times \frac{storecapm3}{storeavailm3 + 1},calculation_knowledge,-1
379,disaster,11,Operational Efficiency Index (OEI),Quantifies operational efficiency based on resource allocation and supply flow,OEI = \frac{deliverysuccessrate}{100} \times \left(1 - \frac{avgdeliveryhours}{24}\right) \times \left(1 + \frac{distributionpoints}{10}\right),calculation_knowledge,-1
380,disaster,12,Communication Security Risk (CSR),Measures the security risk associated with communication systems during disaster response,CSR = secincidentcount \times 5 + (100 - reportcompliance) + 90 - (dataqualityvalue \times 3),calculation_knowledge,-1
381,disaster,13,Personnel Effectiveness Ratio (PER),Evaluates how effectively human resources are utilized in operations,PER = \frac{staffingprofile->>'personnel'->>'total'}{(personnelcostsusd / 10000)} \times \frac{staffingprofile->>'readiness'->>'availability_percent'}{100},calculation_knowledge,-1
382,disaster,14,Market Stability Index (MSI),Assesses the stability and reliability of disaster response operations over time,"MSI = \frac{estdurationdays}{365.0} \times \frac{deliverysuccessrate}{100.0} \times \left(1 - \frac{secincidentcount}{partnerorgs::integer \times 10 + 1}\right) \times 100, \text{ where estdurationdays represents operation duration from operations table, deliverysuccessrate captures logistics reliability from transportation table, and secincidentcount/partnerorgs ratio approximates system disruptions per partner organization}",calculation_knowledge,-1
383,disaster,15,Logistics Performance Metric (LPM),Measures the overall performance of logistics operations,LPM = \frac{totaldeliverytons}{hubcaptons} \times deliverysuccessrate \times \left(1 - \frac{vehiclebreakrate}{100}\right) \times 100,calculation_knowledge,-1
384,disaster,16,Environmental Impact Factor (EIF),Quantifies the environmental footprint of disaster response operations,EIF = carbontons \times \left(1 - \frac{renewenergypct}{100}\right) + (100 - recyclepct) \times 0.5,calculation_knowledge,-1
385,disaster,17,Public Health Resilience Score (PHRS),Evaluates the resilience of public health systems during disasters,PHRS = waterqualityindex \times 0.4 + sanitationcoverage \times 0.3 + vaccinationcoverage \times 0.3,calculation_knowledge,-1
386,disaster,18,Financial Sustainability Ratio (FSR),Assesses the financial sustainability of disaster response operations,FSR = \frac{donorcommitmentsusd}{budgetallotusd} \times \left(1 - \frac{fundsutilpct}{100}\right) - \frac{resourcegapsusd}{budgetallotusd},calculation_knowledge,-1
387,disaster,19,Beneficiary Satisfaction Index (BSI),Measures the satisfaction level of aid recipients,"BSI = benefeedbackscore \times 10 + (commengage\_numeric \times 20) + distequityidx \times 50, \text{ where commengage\_numeric maps Low=1, Medium=2, High=3}",calculation_knowledge,-1
388,disaster,20,Critical Resource Shortage,Identifies situations where essential resources are dangerously depleted,"A condition where storeavailm3 is less than 10% of storecapm3 AND supplyflowstate is 'Strained' or 'Disrupted', indicating severe logistical constraints that may compromise disaster response",domain_knowledge,-1
389,disaster,21,High-Risk Response Operation,Identifies disaster operations with elevated risk factors,"Operations where emerglevel is 'Red' or 'Black' AND safetyranking is 'High Risk' AND secincidentcount > 50, indicating dangerous conditions requiring special safety protocols",domain_knowledge,[1]
390,disaster,22,Resource Optimization Opportunity,Identifies situations where resource allocation could be optimized,"A scenario where hubutilpct < 30 while simultaneously having distributionpoints > 20, suggesting potential for redistribution of resources to maximize efficiency",domain_knowledge,-1
391,disaster,23,Vulnerable Population Hotspot,Identifies areas with highly vulnerable populations requiring priority attention,"Areas where vulnerabilityreview is 'Complete' with distequityidx < 0.3 AND coordeffectlvl is 'Low', indicating populations with high vulnerability and inadequate coordination support",domain_knowledge,[9]
392,disaster,24,Operational Excellence,Identifies disaster response operations demonstrating superior performance,"Operations with deliverysuccessrate > 90 AND invaccpct > 95 AND OEI > 3, representing highly effective logistics and resource management",domain_knowledge,[11]
393,disaster,25,Sustainable Response Operation,Identifies disaster responses with strong sustainability practices,"Operations where EIF < 50 AND renewenergypct > 30 AND recyclepct > 60, demonstrating environmental responsibility during emergency response",domain_knowledge,[16]
394,disaster,26,Public Health Emergency,Identifies situations with severe public health implications,"Conditions where diseaserisk is 'High' AND waterqualityindex < 50 AND PHRS < 40, indicating critical threats to public health requiring immediate intervention",domain_knowledge,[17]
395,disaster,27,Financial Crisis Risk,Identifies operations at risk of financial collapse,"Operations where fundsutilpct > 80 AND fundingstate is 'Critical' AND FSR < 0.2, indicating severe financial strain that threatens operational continuity",domain_knowledge,"[4, 18]"
396,disaster,28,Community Resilience Builder,Identifies operations that strengthen local community capacity,"Operations where localcapacitygrowth is 'Active' AND commengagelvl is 'High' AND BSI > 70, representing efforts that effectively build sustainable community resilience",domain_knowledge,[19]
397,disaster,29,Logistics Breakdown,Identifies severe disruptions in the supply chain,"Situations where LPM < 30 AND lastmilestatus is 'Suspended' AND vehiclebreakrate > 15, indicating critical failures in the logistics system requiring immediate intervention",domain_knowledge,"[3, 15]"
398,disaster,30,Response Time Effectiveness Ratio (RTER),Measures how quickly and effectively disaster response operations are deployed,"RTER = \frac{100}{estdurationdays + 1} \times \frac{deliverysuccessrate}{100} \times \left(\frac{4 - respphase\_numeric}{3}\right), \text{ where respphase\_numeric maps Initial=1, Emergency=2, Recovery=3, Reconstruction=4, else=0, representing faster deployment relative to disaster phase}",calculation_knowledge,[2]
399,disaster,31,Resource Distribution Equity (RDE),Evaluates the fairness of resource distribution across affected areas,"RDE = distequityidx \times \left(1 + \frac{distributionpoints}{20}\right) \times \left(1 - \frac{100 - deliverysuccessrate}{100}\right) \times coordeffect\_factor, \text{ where coordeffect\_factor is 1.2 for High, 1.0 for Medium, 0.8 for Low coordination effectiveness level, and 0 for else}",calculation_knowledge,[9]
400,disaster,32,Communication Resilience Factor (CRF),Measures the resilience of communication systems during disasters,"CRF = 100 - \frac{CSR}{2} \times communication\_factor, \text{ where communication\_factor is 1.0 for Operational, 0.6 for Limited, and 0.3 for Down communication status from impactMetrics.communication}",calculation_knowledge,[12]
401,disaster,33,Supply Chain Sustainability Index (SCSI),Assesses the environmental sustainability of the disaster supply chain,"SCSI = 100 - EIF \times \frac{totaldeliverytons}{1000} \times \left(\frac{fuelefficiencylpk}{20}\right), \text{ where EIF measures the environmental impact and higher scores represent more sustainable supply chains}",calculation_knowledge,[16]
402,disaster,34,Staffing to Need Ratio (SNR),Evaluates whether staffing levels match operational requirements,"SNR = \frac{staffingprofile->>'personnel'->>'total'}{(impactMetrics->>'population'->>'affected'/10000)} \times PER \times ppe\_factor, \text{ where ppe\_factor is 1.2 for Adequate, 0.8 for Limited, 0.5 for Critical PPE status, and 0 for else}",calculation_knowledge,"[8, 13]"
403,disaster,35,Financial Efficiency Metric (FEM),Measures the cost-effectiveness of disaster operations,"FEM = \frac{benefeedbackscore}{costbeneusd} \times FSR \times \left(1 + \frac{OEI}{10}\right), \text{ where higher scores represent more efficient use of financial resources}",calculation_knowledge,"[11, 18]"
404,disaster,36,Logistics Network Resilience (LNR),Quantifies the ability of the logistics network to withstand disruption,"LNR = LPM \times \frac{vehiclecount}{20} \times lastmile\_factor, \text{ where lastmile\_factor is 1.0 for On Track, 0.7 for Delayed, 0.4 for Suspended lastmilestatus, 0 for else}",calculation_knowledge,[15]
405,disaster,37,Health System Capacity Index (HSCI),Measures the disaster area's health system ability to handle medical emergencies,"HSCI = PHRS \times medcap\_numeric \times \left(1 - \frac{hazlevel\_numeric}{6}\right), \text{ where medcap\_numeric maps Adequate=1.0, Limited=0.6, Critical=0.3, else=0 for medical emergency capacity, and hazlevel\_numeric represents severity level from 1-5}",calculation_knowledge,"[0, 17]"
406,disaster,38,Community Engagement Effectiveness (CEE),Evaluates how effectively operations engage with affected communities,"CEE = \frac{BSI}{100} \times commengage\_numeric \times \left(\frac{stakeholdersatisf + 1}{5}\right), \text{ where commengage\_numeric maps Low=1, Medium=2, High=3, else=0 for community engagement level}",calculation_knowledge,[19]
407,disaster,39,Cross-Agency Coordination Index (CACI),Quantifies how effectively multiple agencies coordinate during disaster response,"CACI = \frac{partnerorgs}{10} \times coordeffect\_numeric \times \left(\frac{infosharing\_numeric + 1}{3}\right), \text{ where coordeffect\_numeric maps Low=1, Medium=2, High=3, else=0 and infosharing\_numeric maps Poor=1, Limited=2, Effective=3, else=0}",calculation_knowledge,-1
408,disaster,40,Critical Resource Prioritization Need,Identifies operations requiring immediate resource redistribution,"Operations experiencing Critical Resource Shortage where PER < 0.5 AND emerglevel is either 'Red' or 'Black', indicating a severe mismatch between available resources and critical operational needs",domain_knowledge,"[1, 13, 20]"
409,disaster,41,High-Impact Communication Failure,Identifies disaster areas with severe communication infrastructure breakdown,"Areas where impactMetrics.communication is 'Down' AND CRF < 40 AND coordeffectlvl is 'Low', representing critical communication failures that severely impede response coordination",domain_knowledge,"[5, 9, 32]"
410,disaster,42,Sustainable Operation Excellence,Identifies disaster operations that achieve high performance with minimal environmental impact,"Operations qualifying as Operational Excellence while simultaneously meeting Sustainable Response Operation criteria, representing the gold standard for effective and environmentally responsible disaster management",domain_knowledge,"[24, 25]"
411,disaster,43,Financial Vulnerability Zone,Identifies operations at severe risk due to both funding and resource constraints,"Operations facing Financial Crisis Risk where RUR > 0.9 AND resourceallocstate is 'Critical', indicating dangerous convergence of financial instability and high resource utilization",domain_knowledge,"[6, 10, 27]"
412,disaster,44,Critical Health Response Requirement,Identifies areas needing urgent health system reinforcement,"Areas experiencing Public Health Emergency where HSCI < 30 AND staffingProfile.readiness.ppe_status is 'Critical', indicating severely compromised health response capacity requiring immediate intervention",domain_knowledge,"[8, 26, 37]"
413,disaster,45,Logistics System Collapse Risk,Identifies operations at imminent risk of complete logistics failure,"Operations experiencing Logistics Breakdown where LNR < 20 AND vehiclebreakrate > 25, indicating a logistics system on the verge of complete collapse requiring immediate external support",domain_knowledge,"[29, 36]"
414,disaster,46,Community Resilience Opportunity,Identifies high-potential areas for community resilience building,"Areas where CEE > 2.5 AND vulnerabilityreview is 'Complete' BUT NOT qualifying as Community Resilience Builder, representing opportunities where community engagement is strong but resilience building efforts need strengthening",domain_knowledge,"[28, 38]"
415,disaster,47,Cross-Agency Coordination Crisis,Identifies critical failures in multi-agency coordination,"Situations where CACI < 1.5 AND secincidentcount > 40 AND emerglevel is 'Black', indicating dangerous breakdowns in inter-agency coordination during critical emergency situations",domain_knowledge,"[1, 39]"
416,disaster,48,Resource Distribution Inequity,Identifies operations with significant disparities in resource allocation,"Operations where RDE < 0.4 AND distequityidx < 0.5 AND distributionpoints < 5, indicating serious inequities in how resources reach affected populations",domain_knowledge,[31]
417,disaster,49,Rapid Response Success Model,Identifies exemplary rapid deployment operations,"Operations where RTER > 20 AND OEI > 2.5 AND deliverysuccessrate > 85, representing highly effective and rapidly deployed response operations that can serve as models for future disasters",domain_knowledge,"[11, 30]"
418,disaster,50,Resource Utilization Classification,Categorizes distribution hubs based on their Resource Utilization Ratio (RUR) values,High Utilization (RUR > 5) indicates potentially overloaded hubs that may need resource expansion; Moderate Utilization (2 ≤ RUR ≤ 5) represents optimal resource usage balance; Low Utilization (RUR < 2) indicates underutilized hubs with potential efficiency gains through resource reallocation,domain_knowledge,[10]
419,disaster,51,Environmental Impact Classification,Categorizes operations based on their Environmental Impact Factor (EIF) values,Sustainable (EIF < 50) indicates operations with minimal environmental footprint and strong sustainability practices; Moderate Impact (50 ≤ EIF < 100) represents operations with reasonable environmental management; High Impact (EIF ≥ 100) indicates operations with significant environmental footprint requiring mitigation strategies,domain_knowledge,[16]
420,disaster,52,Community Resilience Classification,Categorizes operations based on their community engagement and resilience-building effectiveness,Operations satisfying criteria from Community Resilience Builder are classified as Community Resilience Builder; operations meeting criteria from Community Resilience Opportunity are classified as Community Resilience Opportunity; all other operations are classified as Standard Operation,domain_knowledge,"[28, 38, 46]"
421,disaster,53,available storage percentage,Calculates what proportion of total storage capacity is currently available,The percentage calculated by dividing available storage (storeavailm3) by total storage capacity (storecapm3) and multiplying by 100,calculation_knowledge,-1
422,robot,0,Robot Age in Years (RAY),Calculates the age of the robot in years based on installation date and record timestamp.,"For a given robot R, let D be the instdateval from robot_details where botdetreg = R, and T be the rects from robot_record where recreg = R. Then, RAY = \frac{(T - D).days}{365.25}",calculation_knowledge,-1
423,robot,1,Average Joint 1 Temperature (AJ1T),Calculates the average temperature of Joint 1 across all operations for a specific robot.,"For a given robot R, AJ1T = \frac{\sum_{jc \in \text{joint_condition} \mid \text{jcdetref = R}} j1tempval}{|\{jc \in \text{joint_condition} \mid \text{jcdetref = R}\}|}",calculation_knowledge,-1
424,robot,2,Maximum Joint Temperature (MJT),Finds the maximum temperature recorded for any joint across all operations for a specific robot.,"For a given robot R, MJT = \max_{jc \in \text{joint_condition} \mid \text{jcdetref = R}} \max(jc.j1tempval, jc.j2tempval, jc.j3tempval, jc.j4tempval, jc.j5tempval, jc.j6tempval)",calculation_knowledge,-1
425,robot,3,Average Position Error (APE),Calculates the average position error across all actuations for a specific robot.,"For a given robot R, APE = \frac{\sum_{ad \in \text{actuation_data} \mid \text{actdetref = R}} poserrmmval}{|\{ad \in \text{actuation_data} \mid \text{actdetref = R}\}|}",calculation_knowledge,-1
426,robot,4,Average TCP Speed (ATCS),Calculates the average speed of the Tool Center Point across all actuations for a specific robot.,"For a given robot R, ATCS = \frac{\sum_{ad \in \text{actuation_data} \mid \text{actdetref = R}} tcpspeedval}{|\{ad \in \text{actuation_data} \mid \text{actdetref = R}\}|}",calculation_knowledge,-1
427,robot,5,Recent Fault Prediction Score (RFPS),The fault prediction score from the most recent maintenance record for a robot.,"For a given robot R, \text{faultpredscore}(mf) \text{ where } mf \in \text{maintenance_and_fault}, \text{upkeeprobot} = R, \text{and } \text{upkeepduedays}(mf) = \min_{mf' \in \text{maintenance_and_fault} \mid \text{upkeeprobot} = R} \text{upkeepduedays}(mf').",calculation_knowledge,-1
428,robot,6,Minimum Remaining Useful Life (MRUL),Finds the minimum Remaining Useful Life across all maintenance records for a specific robot.,"For a given robot R, MRUL = \min_{mf \in \text{maintenance_and_fault} \mid \text{upkeeprobot = R}} rulhours",calculation_knowledge,-1
429,robot,7,Total Operating Hours (TOH),The maximum total operating hours recorded for the robot across all operations.,"For a given robot R, TOH = \max_{o \in \text{operation} \mid \text{operbotdetref = R}} totopshrval",calculation_knowledge,-1
430,robot,8,Number of Operations (NO),Counts the number of operation records for a specific robot.,"For a given robot R, NO = |\{o \in \text{operation} \mid \text{operbotdetref = R}\}|",calculation_knowledge,-1
431,robot,9,Total Program Cycles (TPC),Sums the program cycle counts across all operations for a specific robot.,"For a given robot R, TPC = \sum_{o \in \text{operation} \mid \text{operbotdetref = R}} progcyclecount",calculation_knowledge,-1
432,robot,10,Old Robot,Indicates if a robot is old based on its age.,A robot is considered old if RAY >= 2.,domain_knowledge,[0]
433,robot,11,High Temperature Joint 1,Indicates if Joint 1 has a high average temperature.,Joint 1 has high temperature if AJ1T > 50.,domain_knowledge,[1]
434,robot,12,Overheating Risk,Indicates if there is a risk of overheating based on maximum joint temperature.,There is an overheating risk if MJT > 70.,domain_knowledge,[2]
435,robot,13,Precision Category,Categorizes the precision of the robot based on average position error.,"If APE < 0.1, 'High Precision'; else if APE < 0.5, 'Medium Precision'; else 'Low Precision'.",domain_knowledge,[3]
436,robot,14,Fast Robot,Indicates if the robot operates at high speed based on average TCP speed.,The robot is fast if ATCS > 1000.,domain_knowledge,[4]
437,robot,15,High Fault Risk,Indicates if the robot has a high risk of fault based on average fault prediction score.,The robot has high fault risk if RFPS > 0.5.,domain_knowledge,[5]
438,robot,16,Urgent Maintenance Needed,Indicates if urgent maintenance is needed based on minimum Remaining Useful Life.,Urgent maintenance is needed if MRUL < 100.,domain_knowledge,[6]
439,robot,17,Heavily Used Robot,Indicates if the robot has been heavily used based on total operating hours.,The robot is heavily used if TOH > 10000.,domain_knowledge,[7]
440,robot,18,Multi-Operation Robot,Indicates if the robot has performed multiple operations.,The robot has performed multiple operations if NO > 1.,domain_knowledge,[8]
441,robot,19,High Cycle Count Robot,Indicates if the robot has a high total program cycle count.,The robot has a high cycle count if TPC > 1000000.,domain_knowledge,[9]
442,robot,20,Joint Temperature,Illustrates the temperature values of robot joints.,Joint temperatures are measured in degrees Celsius. Typical operating temperatures range from 20°C to 60°C. Temperatures above 60°C may indicate potential issues.,value_illustration,-1
443,robot,21,Vibration Level,Illustrates the vibration levels of robot joints.,Vibration levels are measured in units such as mm/s. Higher values may indicate wear or misalignment.,value_illustration,-1
444,robot,22,Position Error,Illustrates the position error in robot actuations.,Position error is measured in millimeters. Lower values indicate higher precision.,value_illustration,-1
445,robot,23,TCP Speed,Illustrates the speed of the Tool Center Point.,TCP speed is measured in mm/s. Higher speeds may be required for certain applications but can affect precision.,value_illustration,-1
446,robot,24,Fault Prediction Score,Illustrates the fault prediction score in maintenance records.,"The score ranges from 0 to 1, with higher values indicating a higher likelihood of an impending fault.",value_illustration,-1
447,robot,25,Remaining Useful Life (RUL),Illustrates the Remaining Useful Life in maintenance records.,RUL is measured in hours and indicates the estimated time until the next maintenance is required.,value_illustration,-1
448,robot,26,Safety State,Illustrates the safety state of the robot.,"Possible states include 'Normal', 'Warning', and 'Emergency', indicating the current safety condition.",value_illustration,-1
449,robot,27,Emergency Stops,Illustrates the number of emergency stops recorded.,A higher number of emergency stops may indicate operational issues or safety concerns.,value_illustration,-1
450,robot,28,Tool Wear Percentage,Illustrates the tool wear percentage.,"Tool wear is expressed as a percentage, with 100% indicating the tool needs replacement.",value_illustration,-1
451,robot,29,Controller Load,Illustrates the load value of the system controller.,"Controller load is a measure of CPU or system load, typically ranging from 0 to 100%.",value_illustration,-1
452,robot,30,Weighted Fault Prediction Score (WFPS),"Calculates a weighted average fault prediction score, prioritizing recent records.","For a given robot R, WFPS = \frac{\sum_{mf \in \text{maintenance_and_fault} \mid \text{upkeeprobot = R}} (faultpredscore \cdot w(mf))}{\sum_{mf \in \text{maintenance_and_fault} \mid \text{upkeeprobot = R}} w(mf)}, \text{where } w(mf) = 1 / (1 + \text{upkeepduedays})",calculation_knowledge,-1
453,robot,31,Energy Efficiency Ratio (EER),Measures energy efficiency by comparing energy usage to operating hours.,"For a given robot R, EER = \frac{\sum_{ps \in \text{performance_and_safety} \mid \text{effectivenessrobot = R}} energyusekwhval}{\text{TOH}}, \text{where TOH is used to normalize energy consumption.}",calculation_knowledge,[7]
454,robot,32,Joint Degradation Index (JDI),Computes a composite index of joint health based on temperature and vibration.,"For a given robot R, JDI = \frac{\sum_{jc \in \text{joint_condition} \mid \text{jcdetref = R}} \sum_{i=1}^6 (jitempval_i / \text{MJT} + jivibval_i)}{\text{|}\{jc \in \text{joint_condition} \mid \text{jcdetref = R}\}| \cdot 6}, \text{where MJT normalizes temperatures, and jitempval_i, jivibval_i are joint i's temperature and vibration.}",calculation_knowledge,[2]
455,robot,33,Operation Cycle Efficiency (OCE),Calculates the efficiency of program cycles relative to cycle time.,"For a given robot R, OCE = \frac{\text{TPC}}{\sum_{o \in \text{operation} \mid \text{operbotdetref = R}} cycletimesecval}",calculation_knowledge,[9]
456,robot,34,Safety Incident Score (SIS),Aggregates safety incidents from JSONB safety_metrics.,"For a given robot R, SIS = \sum_{ps \in \text{performance_and_safety} \mid \text{effectivenessrobot = R}} (safety_metrics->>'overloads'::int + safety_metrics->>'collisions'::int + safety_metrics->>'emergency_stops'::int + safety_metrics->>'speed_violations'::int), \text{where JSONB fields are extracted and summed.}",calculation_knowledge,-1
457,robot,35,Maintenance Cost Trend (MCT),Estimates the trend in maintenance costs over time.,"For a given robot R, MCT = \frac{\sum_{mf \in \text{maintenance_and_fault} \mid \text{upkeeprobot = R}} \text{upkeepcostest} \cdot w(mf)}{\text{|}\{mf \in \text{maintenance_and_fault} \mid \text{upkeeprobot = R}\}|}, \text{where } w(mf) = \frac{1}{1 + \text{upkeepduedays}}",calculation_knowledge,-1
458,robot,36,Controller Stress Index (CSI),Measures controller stress based on load and thermal metrics.,"For a given robot R, CSI = \frac{\sum_{sc \in \text{system_controller} \mid \text{systemoverseerrobot = R}} (controller_metrics->>'load_value'::float + controller_metrics->>'thermal_level'::float)}{\text{|}\{sc \in \text{system_controller} \mid \text{systemoverseerrobot = R}\}|}, \text{where JSONB fields load_value and thermal_level are averaged.}",calculation_knowledge,-1
459,robot,37,Tool Wear Rate (TWR),Estimates the rate of tool wear relative to program cycles.,"For a given robot R, TWR = \frac{\sum_{ps \in \text{performance_and_safety} \mid \text{effectivenessrobot = R}} toolwearpct}{\text{TPC}}, \text{where TPC normalizes tool wear percentage.}",calculation_knowledge,[9]
460,robot,38,Joint Torque Variance (JTV),Calculates the variance of joint torques to assess operational stability.,"For a given robot R, JTV = \frac{\sum_{jp \in \text{joint_performance} \mid \text{jperfdetref = R}} \sum_{i=1}^6 ((joint_metrics->>'jointi'->>'torque'::float - \mu_i)^2)}{\text{|}\{jp \in \text{joint_performance} \mid \text{jperfdetref = R}\}| \cdot 6}, \text{where } \mu_i = \frac{\sum_{jp} joint_metrics->>'jointi'->>'torque'::float}{\text{|}\{jp\}|}, \text{and jointi is joint i's metrics.}",calculation_knowledge,-1
461,robot,39,Payload Utilization Ratio (PUR),Measures how close the robot operates to its payload capacity.,"For a given robot R, PUR = \frac{\sum_{ad \in \text{actuation_data} \mid \text{actdetref = R}} payloadwval}{\text{(robot_details.payloadcapkg where botdetref = R)} \cdot \text{|}\{ad \in \text{actuation_data} \mid \text{actdetref = R}\}|}, \text{where RAY adjusts for age-related capacity changes if applicable.}",calculation_knowledge,[0]
462,robot,40,Maintenance Priority Level,Classifies robots into maintenance priority categories based on fault prediction scores and remaining useful life,"For a robot R, the Maintenance Priority Level is:
- 'CRITICAL' if WFPS > 0.6 AND MRUL < 500
- 'WARNING' if WFPS > 0.4 OR MRUL < 500
- 'NORMAL' otherwise",domain_knowledge,"[30, 6]"
463,robot,41,Energy Inefficient Robot,Flags robots with poor energy efficiency.,A robot R is Energy Inefficient if EER > 0.01 and TOH > 1000.,domain_knowledge,"[31, 7]"
464,robot,42,Joint Health Risk,Indicates robots at risk of joint failure.,A robot R has Joint Health Risk if JDI > 1.5 and MJT > 65.,domain_knowledge,"[32, 2]"
465,robot,43,Cycle Efficiency Category,Categorizes robots based on their program cycle efficiency performance,"For a robot R, the Cycle Efficiency Category is:
- 'Low Efficiency' if OCE < 100 AND TPC > 500000
- 'Medium Efficiency' if OCE < 150 OR TPC > 300000
- 'High Efficiency' otherwise",domain_knowledge,"[33, 9]"
466,robot,44,High Safety Concern,Flags robots with significant safety incidents.,A robot R has High Safety Concern if SIS > 20.,domain_knowledge,[34]
467,robot,45,Escalating Maintenance Costs,Identifies robots with rising maintenance costs.,A robot R has Escalating Maintenance Costs if MCT > 500 and RAY > 2.,domain_knowledge,"[35, 0]"
468,robot,46,Controller Overload Risk,Indicates robots with stressed controllers.,A robot R has Controller Overload Risk if CSI > 100 and NO > 2.,domain_knowledge,"[36, 8]"
469,robot,47,Tool Replacement Status,Classifies robots into tool replacement priority categories based on tool wear rate and program cycles,"For a robot R, the Tool Replacement Status is:
- 'URGENT' if TWR > 0.001 AND TPC > 10000
- 'WARNING' if TWR > 0.0005 OR average tool wear percentage > 75
- 'NORMAL' otherwise",domain_knowledge,"[37, 9]"
470,robot,48,Operational Instability,Identifies robots with unstable joint performance.,A robot R has Operational Instability if JTV > 50 and MJT > 60.,domain_knowledge,"[38, 2]"
471,robot,49,Overloaded Robot,Flags robots operating near or beyond payload capacity.,A robot R is Overloaded if PUR > 0.9 and RAY > 1.,domain_knowledge,"[39, 0]"
472,robot,50,JDI-TOH Regression Slope,Measures the linear relationship between Joint Degradation Index and Total Operating Hours using regression.,"For a set of robots meeting JDI > 1.5, MJT > 65, and TOH > 5000, the JDI-TOH Regression Slope is given by \[ \text{slope} = \frac{n \sum (x_i y_i) - \sum x_i \sum y_i}{n \sum x_i^2 - (\sum x_i)^2} \] where \( y_i = \text{JDI} \) (Joint Degradation Index for robot \( i \)), \( x_i = \text{TOH} \) (Total Operating Hours for robot \( i \)), \( n \) is the number of qualifying robots, and sums are over all qualifying robots.",calculation_knowledge,"[32, 7]"
473,robot,51,Average Cycle Time,Calculates the average time in seconds for completing one program cycle,"For a given robot R, Average Cycle Time = \frac{\sum_{o \in \text{operation} \mid \text{operbotdetref = R}} \text{cycletimesecval}}{|\{o \in \text{operation} \mid \text{operbotdetref = R}\}|}",calculation_knowledge,-1
474,robot,52,EER Rank,Ranks the Energy Efficiency Ratio within each application type to assess relative efficiency.,EER Rank = \text{PERCENT_RANK}() \text{ OVER } (\text{PARTITION BY } apptypeval \text{ ORDER BY } EER \text{ DESC})},calculation_knowledge,[31]
475,robot,53,APE Rank,Ranks the Average Position Error within each controller type to assess relative precision.,"For a given robot R, APE Rank = \text{PERCENT_RANK}() \text{ OVER } (\text{PARTITION BY } ctrltypeval \text{ ORDER BY } APE \text{ DESC})",calculation_knowledge,[3]
476,robot,54,Program Efficiency Rank,Ranks programs based on their average Operation Cycle Efficiency across robots.,"For a program P, Program Efficiency Rank = \text{DENSE_RANK}() \text{ OVER } (\text{ORDER BY } \text{AVG(program_oce)} \text{ DESC}), \text{where program_oce is the Operation Cycle Efficiency for program P on each robot.}",calculation_knowledge,[33]
477,robot,55,Efficiency Metrics,"Aggregates efficiency-related metrics, including the most efficient program and average program Operation Cycle Efficiency.","For a model series, Efficiency Metrics = \text{jsonb_build_object}('most_efficient_program', \text{currprogval with Program Efficiency Rank = 1}, 'avg_program_efficiency', \text{AVG(avg_program_oce)}), \text{where avg_program_oce is the average Operation Cycle Efficiency per program.}",calculation_knowledge,[33]
478,robot,56,Robot Count,"The number of distinct robots within a specific group (e.g., model series).","Let M be the model series, \mathcal{R}_M be the set of unique robot identifiers in M. Then Robot Count = |\mathcal{R}_M|",calculation_knowledge,-1
479,robot,57,Model Average Position Error,The average of the Average Position Error values for all robots within a specific model series.,"Let M be the model series, \mathcal{R}_M be the set of unique robot identifiers in M. Then Model Avg Pos Error = \frac{\sum_{R \in \mathcal{R}_M} \text{APE}(R)}{|\mathcal{R}_M|}",calculation_knowledge,[3]
480,robot,58,Model Average TCP Speed,The average of the Average TCP Speed values for all robots within a specific model series.,"Let M be the model series, \mathcal{R}_M be the set of unique robot identifiers in M. Then Model Avg TCP Speed = \frac{\sum_{R \in \mathcal{R}_M} \text{ATCS}(R)}{|\mathcal{R}_M|}",calculation_knowledge,[4]
481,robot,59,Model Average Max Operating Hours,The average of the maximum Total Operating Hours recorded for each robot within a specific model series.,"Let M be the model series, \mathcal{R}_M be the set of unique robot identifiers in M. Then Model Avg Max Ops Hours = \frac{\sum_{R \in \mathcal{R}_M} \text{TOH}(R)}{|\mathcal{R}_M|}",calculation_knowledge,[7]
482,polar,0,Equipment Efficiency Rating (EER),"A composite metric that evaluates the overall efficiency of equipment based on performance, reliability, and environmental impact.","EER = \frac{performanceindex + reliabilityindex}{2} \times (1 - \frac{environmentalimpactindex}{10}), \text{ where higher values indicate more efficient equipment with better performance and lower environmental impact.}",calculation_knowledge,-1
483,polar,1,Operational Readiness Score (ORS),Quantifies how ready equipment is for immediate deployment based on operational status and maintenance schedule.,ORS = \begin{cases} 10 \times (1 - \frac{operationhours}{maintenancecyclehours}) & \text{if operationalstatus = 'Active'} \\ 5 \times (1 - \frac{operationhours}{maintenancecyclehours}) & \text{if operationalstatus = 'Standby'} \\ 0 & \text{otherwise} \end{cases},calculation_knowledge,-1
484,polar,2,Energy Sustainability Index (ESI),Measures the sustainability of an equipment's energy usage by evaluating energy efficiency and renewable sources.,"ESI = energyefficiencypercent \times \begin{cases} 1.5 & \text{if powersource IN ('Solar', 'Wind')} \\ 1.2 & \text{if powersource = 'Hybrid'} \\ 1.0 & \text{if powersource = 'Battery'} \\ 0.7 & \text{if powersource = 'Diesel'} \\ 0 & \text{otherwise} \end{cases}, \text{ providing higher ratings for renewable energy and lower ratings for fossil fuels.}",calculation_knowledge,-1
485,polar,3,Structural Safety Factor (SSF),Evaluates the safety margin of structures under extreme weather conditions.,SSF = \frac{100 - structuralloadpercent}{100} \times \begin{cases} 0.5 & \text{if snowloadkgm2 > 100 or windspeedms > 20} \\ 0.8 & \text{if snowloadkgm2 > 50 or windspeedms > 10} \\ 1.0 & \text{otherwise} \end{cases},calculation_knowledge,-1
486,polar,4,Communication Reliability Index (CRI),Assesses the reliability of communication systems based on signal metrics and antenna status.,"CRI = \begin{cases} 0 & \text{if antennastatus = 'Error'} \\ 5 & \text{if antennastatus = 'Warning'} \\ 10 & \text{if antennastatus = 'Normal'} \\ 0 & \text{otherwise} \end{cases} \times (1 - \frac{signalmetrics.latency\_ms}{1000}), \text{ where lower latency and better antenna status result in higher reliability.}",calculation_knowledge,-1
487,polar,5,Vehicle Performance Coefficient (VPC),A metric that evaluates vehicle performance based on mechanical condition and operational efficiency.,"VPC = (1 - \frac{brakepadwearpercent + trackwearpercent}{200}) \times \frac{vehiclespeedkmh}{50} \times \frac{engineloadpercent}{100}, \text{ where lower wear percentages and optimal engine load contribute to better performance.}",calculation_knowledge,-1
488,polar,6,Thermal Insulation Efficiency (TIE),Measures how effectively a structure retains heat based on insulation status and heat loss rate.,"TIE = \begin{cases} 0.9 - \frac{heatlossratekwh}{10} & \text{if insulationstatus = 'Good'} \\ 0.6 - \frac{heatlossratekwh}{10} & \text{if insulationstatus = 'Fair'} \\ 0.3 - \frac{heatlossratekwh}{10} & \text{if insulationstatus = 'Poor'} \end{cases}, \text{ where lower heat loss and better insulation result in higher efficiency.}",calculation_knowledge,-1
489,polar,7,Water Resource Management Index (WRMI),"Evaluates the efficiency of water resource management based on water levels, quality, and waste levels.","WRMI = waterlevelpercent \times \frac{waterqualityindex}{100} \times (1 - \frac{wastetanklevelpercent}{100}), \text{ where higher water quality and appropriate water/waste levels indicate better management.}",calculation_knowledge,-1
490,polar,8,Scientific Equipment Reliability (SER),Quantifies the reliability of scientific equipment based on calibration status and measurement accuracy.,"SER = measurementaccuracypercent \times \begin{cases} 1.0 & \text{if calibrationstatus = 'Valid'} \\ 0.7 & \text{if calibrationstatus = 'Due'} \\ 0.3 & \text{if calibrationstatus = 'Expired'} \end{cases}, \text{ where valid calibration and high accuracy result in more reliable scientific data.}",calculation_knowledge,-1
491,polar,9,Renewable Energy Contribution (REC),Calculates the percentage contribution of renewable energy sources to the total power generation.,"REC = \frac{renewablemetrics.solar.output\_w + renewablemetrics.wind.output\_w}{fuelcelloutputw + renewablemetrics.solar.output\_w + renewablemetrics.wind.output\_w} \times 100, \text{ where higher values indicate greater reliance on renewable energy.}",calculation_knowledge,-1
492,polar,10,Extreme Weather Readiness (EWR),Evaluates how prepared equipment and structures are for extreme weather conditions.,"A composite rating where equipment is considered 'Extreme Weather Ready' if it maintains an SSF > 0.7 and has operational heating systems (heaterstatus not 'Off'), proper insulation (insulationstatus not 'Poor'), and functional emergency systems (emergencylightstatus = 'On' or 'Testing').",domain_knowledge,[3]
493,polar,11,Critical Equipment,Identifies equipment that is essential for life support and safety in polar environments.,"Equipment is designated as 'Critical' if it belongs to the 'Safety' equipment type, has a safety index > 0.8, and is associated with any of these life-critical systems: lifesupportstatus, oxygensupplystatus, or heater systems where temperatures are below freezing (externaltemperaturec < 0).",domain_knowledge,-1
494,polar,12,Maintenance Priority Level,Classifies equipment based on the urgency of required maintenance.,"Equipment is categorized into maintenance priority levels: 'Immediate Attention' (operationhours > maintenancecyclehours OR operationalstatus = 'Repair'), 'Scheduled Service' (operationhours > 0.8 * maintenancecyclehours), and 'Routine Maintenance' (all other cases), helping prioritize resource allocation.",domain_knowledge,-1
495,polar,13,Energy Sustainability Classification,Categories equipment based on their energy sustainability index for environmental impact assessment.,"Equipment is classified as 'Green' (ESI > 0.8), 'Intermediate' (ESI between 0.4 and 0.8), or 'High Impact' (ESI < 0.4), with Green indicating environmentally sustainable operations.",domain_knowledge,[2]
496,polar,14,Communication Zone Status,Evaluates the communication coverage and reliability in different operational zones.,"A zone is classified as having 'Reliable Coverage' when equipment within it maintains a CRI > 7, has active satellite connections (signalmetrics.satellite_status = 'Connected'), and supports emergency beacon functionality (emergencybeaconstatus != 'Inactive').",domain_knowledge,[4]
497,polar,15,Vehicle Operational Safety Threshold,Defines the safety threshold for vehicle operations based on multiple safety factors.,"A vehicle is considered 'Safe for Operation' when it maintains a VPC > 0.6, has brake fluid levels above 50%, brake pad wear below 70%, adequate tire pressure (tiremetrics.pressure_kpa > 200), and is operated within recommended load limits (vehicleloadkg within manufacturer specifications).",domain_knowledge,[5]
498,polar,16,Scientific Data Reliability Classification,Classifies scientific data based on equipment reliability and calibration status.,"Scientific data is classified as 'Research Grade' when collected by equipment with SER > 0.9, with valid calibration status, and under appropriate environmental conditions for the equipment type.",domain_knowledge,[8]
499,polar,17,Cabin Habitability Standard,Defines the minimum standards for habitable cabin conditions in polar environments.,"A cabin meets 'Habitability Standards' when it maintains internal temperature (cabinclimate.temperature_c) between 18-24°C, oxygen levels (cabinclimate.o2_percent) above 19.5%, CO2 levels (cabinclimate.co2_ppm) below 1000 ppm, functioning ventilation systems, and operational heating systems.",domain_knowledge,-1
500,polar,18,Water Conservation Requirement,Specifies the conditions under which water conservation measures must be implemented.,"Water conservation measures must be implemented when the WRMI falls below 0.5, indicating either low water levels, poor water quality, or high waste tank levels that require immediate attention to maintain sustainable water usage.",domain_knowledge,[7]
501,polar,19,Sustainable Energy Operation,Defines the conditions for energy-sustainable operations in polar environments.,An operation is considered 'Energy-Sustainable' when it maintains a REC above 70% (meaning more than 70% of energy comes from renewable sources) while maintaining full operational capability and adequate power reserves for at least 48 hours in case of emergency.,domain_knowledge,[9]
502,polar,20,reliabilityindex,Illustrates the significance of reliability index measurements in equipment durability.,"The reliability index typically ranges from 0 to 1, where values below 0.5 indicate equipment that fails frequently and requires constant maintenance, values between 0.5-0.8 represent equipment with occasional failures that require regular maintenance, and values above 0.8 indicate highly reliable equipment with minimal downtime.",value_illustration,-1
503,polar,21,operationhours,Illustrates the meaning and importance of equipment operation hours.,"Operation hours represent the cumulative time an equipment has been in active use. New equipment typically has low hours (0-100), mid-life equipment shows moderate hours (100-1000), while equipment approaching maintenance or replacement typically exceeds 1000 hours. The ratio of operation hours to maintenance cycle hours is critical for preventative maintenance scheduling.",value_illustration,-1
504,polar,22,externaltemperaturec,Illustrates the significance of external temperature readings in polar environments.,"External temperature in polar regions typically ranges from -70°C to 10°C. Temperatures below -40°C represent extreme cold requiring special equipment protection measures, -20°C to -40°C require standard cold weather protocols, while temperatures above -20°C are considered relatively mild for polar operations but still require normal cold weather precautions.",value_illustration,-1
505,polar,23,windspeedms,Illustrates the impact of wind speed measurements on operations and safety.,"Wind speeds in polar environments typically range from 0 to 60 m/s. Speeds below 5 m/s represent calm conditions, 5-15 m/s indicate moderate winds with minor operational impact, 15-25 m/s represent strong winds requiring additional safety measures, and speeds above 25 m/s indicate dangerous conditions that may require suspension of outdoor activities and securing of equipment.",value_illustration,-1
506,polar,24,powerconsumptionw,Illustrates the significance of power consumption measurements in energy management.,"Power consumption measured in watts varies by equipment type. Small scientific instruments typically consume 5-50W, communication equipment 20-200W, heating systems 500-5000W, and vehicle systems 1000-10000W. Understanding consumption patterns is crucial for power budgeting and determining appropriate power source sizing in isolated polar environments.",value_illustration,-1
507,polar,25,Water Quality Classification System (WQCS),"A standardized system that categorizes water quality for health, safety, and operational purposes in polar environments.","Water is classified into five quality categories based on its quality index: 'High-Quality' WHEN (waterqualityindex >= 91), suitable for all purposes including direct consumption; 'Good' WHEN (waterqualityindex >= 71 AND waterqualityindex < 91), safe for consumption after standard treatment; 'Moderate' WHEN (waterqualityindex >= 51 AND waterqualityindex < 71), acceptable for washing but not consumption; 'Poor' WHEN (waterqualityindex >= 26 AND waterqualityindex < 51), suitable only for limited non-contact uses; 'Unsafe' WHEN (waterqualityindex < 26), unsuitable for any use and requiring immediate remediation.",domain_knowledge,-1
508,polar,26,cabinclimate.co2_ppm,Illustrates the health and cognitive implications of carbon dioxide levels in enclosed environments.,"CO2 levels in enclosed spaces like cabins are measured in parts per million (ppm). Levels below 600 ppm indicate excellent ventilation, 600-1000 ppm represent good air quality, 1000-2500 ppm indicate poor ventilation that may cause drowsiness and reduced cognitive function, while levels above 2500 ppm may cause headaches, sleepiness, and significantly impaired cognitive performance.",value_illustration,-1
509,polar,27,energyefficiencypercent,Illustrates the meaning and importance of energy efficiency percentages.,"Energy efficiency percentage typically ranges from 10% to 99%. Values below 30% indicate inefficient systems typical of older equipment, 30-60% represent standard efficiency for conventional equipment, 60-80% indicate high-efficiency modern systems, while values above 80% represent cutting-edge technology with optimal efficiency that minimizes energy waste and operational costs.",value_illustration,-1
510,polar,28,safetyindex,Illustrates the significance of safety index ratings for operational risk assessment.,"Safety index typically ranges from 0 to 1, where values below 0.5 indicate equipment with significant safety concerns requiring immediate attention or limited operation, values between 0.5-0.7 represent equipment with acceptable safety for normal operations with appropriate precautions, and values above 0.7 indicate equipment with excellent safety features suitable for all operational conditions including those with elevated risks.",value_illustration,-1
511,polar,29,fuelcellefficiencypercent,Illustrates the technical significance of fuel cell efficiency ratings.,"Fuel cell efficiency percentages typically range from 40% to 90%. Values below 50% represent older or degraded fuel cell technology, 50-70% indicate standard efficiency modern fuel cells suitable for general applications, and values above 70% represent high-performance fuel cells with optimal conversion of chemical energy to electrical power with minimal waste heat generation.",value_illustration,-1
512,polar,30,Overall Safety Performance Index (OSPI),Comprehensively evaluates equipment's overall safety performance based on safety index and equipment efficiency rating,OSPI = safetyindex × EER × 0.8,calculation_knowledge,[0]
513,polar,31,Polar Transportation Efficiency Coefficient (PTEC),"Measures vehicle transportation efficiency in polar conditions, considering vehicle performance and energy sustainability",PTEC = VPC × (0.6 + 0.4 × ESI ÷ 100),calculation_knowledge,"[2, 5]"
514,polar,32,Base Station Communication Stability Index (BSCSI),Evaluates the stability and reliability of polar base station communication systems,BSCSI = CRI × (1 + 0.2 × signalmetrics.radio_strength_dbm ÷ 100) × (1 - 0.01 × (1000 - signalmetrics.latency_ms)),calculation_knowledge,[4]
515,polar,33,Life Support System Reliability (LSSR),Evaluates the reliability of life support systems under polar conditions,LSSR = 0.7 × ORS + 0.3 × TIE,calculation_knowledge,"[1, 6]"
516,polar,34,Scientific Mission Success Probability (SMSP),Predicts the probability of successful completion of scientific missions,SMSP = SER × (0.8 + 0.2 × CRI ÷ 10),calculation_knowledge,"[4, 8]"
517,polar,35,Resource Self-Sufficiency Index (RSSI),Measures a polar site's self-sufficiency in terms of resources,RSSI = 0.6 × REC + 0.4 × WRMI,calculation_knowledge,"[7, 9]"
518,polar,36,Extreme Climate Adaptation Coefficient (ECAC),Evaluates equipment adaptation capability under extreme climate conditions,ECAC = SSF × (1 + TIE × 0.5) × \begin{cases} 0.7 & \text{if externaltemperaturec < -30} \\ 0.85 & \text{if externaltemperaturec < -15} \\ 1.0 & \text{otherwise} \end{cases},calculation_knowledge,"[3, 6]"
519,polar,37,Long-term Operational Stability Score (LOSS),Evaluates the stability of equipment during long-term operation,LOSS = 0.5 × EER + 0.5 × ORS × (1 - \frac{operationhours}{20000}),calculation_knowledge,"[0, 1]"
520,polar,38,Energy-Water Resource Integration Index (EWRII),Evaluates the integration efficiency of energy and water resource management,EWRII = 0.5 × ESI + 0.5 × WRMI × (1 - \frac{heatertemperaturec}{100}),calculation_knowledge,"[2, 7]"
521,polar,39,Comprehensive Operational Reliability Indicator (CORI),Comprehensively assesses the overall reliability of polar equipment operations,CORI = 0.4 × EER + 0.4 × ORS + 0.2 × CRI,calculation_knowledge,"[0, 1, 4]"
522,polar,40,Extreme Operating Conditions (EOC),Defines the extreme environmental conditions under which equipment can safely operate,Equipment is considered to 'operate safely under extreme conditions' when its SSF > 0.65 and ECAC > 0.8.,domain_knowledge,"[3, 36]"
523,polar,41,Emergency Response Readiness Status (ERRS),Assesses a polar site's preparedness to respond to emergency situations,"A polar site is rated as 'emergency response ready' when its critical equipment maintains OSPI > 0.75 and LSSR > 0.8, with emergencycommunicationstatus = 'Operational' and backuppowerstatus = 'Active' and batterystatus.level_percent > 85.",domain_knowledge,"[30, 33]"
524,polar,42,Sustainable Polar Operations (SPO),Defines sustainability standards for polar operations,"Polar operations are defined as 'sustainable' when the site maintains RSSI > 0.7 and EWRII > 0.65, with wastemanagementstatus = 'Normal' and environmentalimpactindex < 6.0.",domain_knowledge,"[35, 38]"
525,polar,43,Critical Scientific Equipment Status (CSES),Determines the operational status and reliability of critical scientific equipment,"Scientific equipment is classified as 'Fully Operational' (SER > 0.9 and SMSP > 0.85), 'Degraded Operation' (SER > 0.7 and SMSP > 0.6), or 'Needs Repair' (other cases).",domain_knowledge,"[8, 34]"
526,polar,44,Polar Vehicle Safe Operation Conditions (PVSOC),Determines the conditions for safe operation of polar vehicles,"Polar vehicles are considered 'suitable for polar missions' when they maintain PTEC > 0.7 and VPC > 0.75, with operationalstatus = 'Active' and safetyindex ≥ 0.8.",domain_knowledge,"[5, 31]"
527,polar,45,Communication Network Resilience Assessment (CNRA),Assesses the resilience and interference resistance of polar communication networks,"Communication networks are assessed as having 'High Resilience' (CRI > 0.8 and BSCSI > 0.85), 'Medium Resilience' (CRI > 0.6 and BSCSI > 0.7), or 'Low Resilience' (other cases).",domain_knowledge,"[4, 32]"
528,polar,46,Critical Infrastructure Protection Level (CIPL),Determines the protection level for polar critical infrastructure,"Infrastructure is assigned protection level 'A' (SSF > 0.8, LOSS > 0.85, and OSPI > 0.9), 'B' (SSF > 0.7, LOSS > 0.75, and OSPI > 0.8), or 'C' (other cases).",domain_knowledge,"[3, 30, 37]"
529,polar,47,Long-term Scientific Mission Viability (LSMV),Assesses the viability of long-term scientific missions under polar conditions,"Scientific missions are assessed as 'long-term viable' when all involved scientific equipment maintains SMSP > 0.8 and overall site operations maintain CORI > 0.75, with calibrationstatus = 'Valid' and dataloggingstatus = 'Active'.",domain_knowledge,"[34, 39]"
530,polar,48,Polar Base Energy Security Status (PBESS),Determines the security status of energy supply for polar bases,"A polar base is assessed as being in an 'energy secure' state when it maintains REC > 65%, ESI > 0.7, and RSSI > 0.75, with batterystatus.level_percent > 75 and hydrogenlevelpercent > 70.",domain_knowledge,"[2, 9, 35]"
531,polar,49,Comprehensive Environmental Adaptability Rating (CEAR),Assesses the overall adaptability of equipment and systems to the polar environment,"Equipment and systems are rated as having 'Excellent Adaptability' (ECAC > 0.85 and SSF > 0.8 and thermalsolarwindandgrid.insulationstatus = 'Good'), 'Good Adaptability' (ECAC > 0.7 and SSF > 0.65 and thermalsolarwindandgrid.insulationstatus != 'Poor'), or 'Limited Adaptability' (other cases).",domain_knowledge,"[10, 36]"
532,polar,50,Extreme Weather Readiness Status (EWRS),A binary classification system that determines if equipment has met all necessary conditions to safely operate during extreme weather events.,"Equipment is classified as 'Extreme Weather Ready' WHEN (SSF > 0.7) AND (heaterstatus != 'Off') AND (insulationstatus != 'Poor') AND (emergencylightstatus IN ('On', 'Testing')); OTHERWISE equipment is classified as 'Not Ready'. This evaluation combines structural integrity checks with essential operational systems status to determine immediate readiness for extreme weather exposure.",domain_knowledge,[3]
533,polar,51,Life Support Reliability Classification (LSRC),Categorizes life support systems into reliability classes based on their LSSR score for operational decision-making.,"Life support systems are classified into three reliability categories: 'High Reliability' WHEN (LSSR >= 0.8), 'Moderate Reliability' WHEN (LSSR >= 0.6 AND LSSR < 0.8), and 'Low Reliability' WHEN (LSSR < 0.6).",domain_knowledge,[33]
534,polar,52,Energy Sustainability Classification System (ESCS),A comprehensive classification system that categorizes operational energy sustainability based on renewable energy contribution percentages.,Operations are classified into three sustainability levels: 'Energy-Sustainable' WHEN (REC > 70); 'Moderately Sustainable' WHEN (REC > 50 AND REC <= 70); 'Low Sustainability' WHEN (REC <= 50).,domain_knowledge,"[9, 19]"
535,polar,53,Water Resource Management Status Classification (WRMSC),A comprehensive classification system that categorizes water resource management status based on WRMI values to guide operational decisions.,"Water management operations are classified into three status levels: 'Conservation Needed' WHEN (WRMI < 0.5), indicating critical resource limitations requiring immediate conservation measures; 'Monitoring Advised' WHEN (WRMI >= 0.5 AND WRMI < 0.7), representing adequate but vigilant management requiring regular system monitoring; 'Sustainable Management' WHEN (WRMI >= 0.7), indicating optimal water resource utilization suitable for unrestricted operations.",domain_knowledge,"[7, 18]"
536,insider,0,Daily Turnover Rate (DTR),"Calculates the ratio of a trader's daily trading volume to their account balance, indicating capital velocity.",DTR = \frac{\text{voldaily}}{\text{acctbal}},calculation_knowledge,-1
537,insider,1,Order Modification Intensity (OMI),Measures how frequently a trader modifies orders relative to their cancellation rate.,OMI = \frac{\text{modfreq}}{1 - \text{cancelpct}} \text{ (undefined if cancelpct = 1)},calculation_knowledge,-1
538,insider,2,Trader Leverage Exposure (TLE),Extracts the leverage ratio from the trader's performance data.,TLE = \text{trading_performance.risklevel.levratio},calculation_knowledge,-1
539,insider,3,Suspicious Activity Index (SAI),A composite index attempting to quantify overall suspicious trading behavior based on risk indicators.,"SAI = (w_1 \times \text{SpoofProbNorm}) + (w_2 \times \text{FrontScoreNorm}) + (w_3 \times \text{QStuffNorm}) + (w_4 \times \text{WashSusNorm}) + (w_5 \times \text{LayerIndNorm}) \\ \text{where } \text{SpoofProbNorm} = \frac{\text{risk_indicators.spoofprob}}{100} \\ \text{FrontScoreNorm} = \frac{\text{risk_indicators.frontscore}}{100} \text{ (assuming max score is 100)} \\ \text{QStuffNorm} = \text{MinMaxScale}(\text{risk_indicators.qstuffindex}) \\ \text{WashSusNorm} = \text{MapToNumeric}(\text{risk_indicators.washsus}, {'Low': 0.1, 'Medium': 0.5, 'High': 1.0}) \\ \text{LayerIndNorm} = \text{MapToNumeric}(\text{risk_indicators.layerind}, {'None': 0.0, 'Suspected': 0.5, 'Confirmed': 1.0}) \\ w_i \text{ are weights assigned based on importance, summing to 1.}",calculation_knowledge,-1
540,insider,4,Pattern Anomaly Score (PAS),"Measures the deviation of a trader's pattern similarity from their peer correlation, potentially indicating unique illicit behavior.",PAS = |\text{patsim} - \text{peercorr}|,calculation_knowledge,-1
541,insider,5,Compliance Recidivism Score (CRS),"Calculates a score indicating the tendency for repeat compliance issues, adjusted for account age.","CRS = \frac{\text{prevviol}}{\text{Max}(1, \frac{\text{acctdays}}{365})} \text{ (joining compliancecase to trader via transactionrecord)}",calculation_knowledge,-1
542,insider,6,Investigation Intensity Index (III),Combines behavioral and network analysis scores from an investigation.,III = (0.6 \times \text{behansc}) + (0.4 \times \text{netansc}),calculation_knowledge,-1
543,insider,7,Sentiment Divergence Factor (SDF),Measures the difference between news and social media sentiment scores.,SDF = |\text{newsscore} - \text{socscore}|,calculation_knowledge,-1
544,insider,8,Relative Short Interest (RSI),Calculates short interest ratio relative to institutional ownership.,RSI = \frac{\text{shortintrt}}{\text{instownpct}} \text{ (undefined if instownpct = 0)},calculation_knowledge,-1
545,insider,9,Enforcement Financial Impact Ratio (EFIR),Calculates the ratio of the penalty amount to the trader's account balance at the time of the related transaction.,EFIR = \frac{\text{penamt}}{\text{acctbal}},calculation_knowledge,-1
546,insider,10,High-Risk Trader Profile,Identifies traders exhibiting characteristics associated with high-risk trading strategies.,A trader is considered High-Risk if their TLE > 5.0 AND their `trading_performance.risklevel.risklevel` is 'Aggressive' OR their DTR > 0.5.,domain_knowledge,"[0, 2]"
547,insider,11,Potential Insider Trading Flag,Flags transactions potentially linked to insider knowledge based on timing and context.,A transaction is flagged if `infoleaksc` > 50.0 AND `corpeventprx` is NOT NULL AND `eventannotm` is 'Pre-market' or 'Intraday'.,domain_knowledge,"[33, 34]"
548,insider,12,Market Manipulation Pattern: Layering/Spoofing,Identifies trading sessions indicative of layering or spoofing tactics.,A transaction record suggests Layering/Spoofing if `risk_indicators.layerind` is 'Confirmed' OR (`risk_indicators.spoofprob` > 0.75 AND OMI > 1.0).,domain_knowledge,"[1, 31, 32]"
549,insider,13,Collusion Network Indicator,Suggests potential collusion based on investigation details.,A case indicates potential collusion if `tcirclesz` > 5 AND `grpbehsc` > 0.6 AND `commpat` is 'Regular'.,domain_knowledge,[37]
550,insider,14,Elevated Regulatory Scrutiny,Identifies compliance cases under intense review or investigation.,A case is under Elevated Regulatory Scrutiny if `alertlvl` is 'High' or 'Critical' AND `invstprior` is 'High' AND `monitint` is 'Intensive'.,domain_knowledge,[35]
551,insider,15,Problematic Compliance History,Identifies traders with a poor track record of compliance.,A trader has a Problematic Compliance History if they have `prevviol` > 3 OR their `comprate` is 'C' or 'D' OR their CRS > 1.0.,domain_knowledge,"[5, 36]"
552,insider,16,Wash Trading Alert,Flags transactions highly suspicious for wash trading.,A transaction triggers a Wash Trading Alert if `risk_indicators.washsus` is 'High'.,domain_knowledge,[30]
553,insider,17,Event-Driven Trader,Classifies traders whose activity appears strongly linked to corporate events.,A trader may be classified as Event-Driven if a significant portion (>30%) of their transactionrecord entries (linked via trdref) have a non-null corpeventprx (joined via transref).,domain_knowledge,[33]
554,insider,18,High Cancellation/Modification Trader,"Identifies traders who frequently cancel or modify orders, potentially indicating manipulative intent or poor execution strategy.",A trader is flagged if their average cancelpct > 0.5 OR their average OMI > 1.5 across their transactions.,domain_knowledge,[1]
555,insider,19,Significant Enforcement Action,Categorizes enforcement actions that represent substantial penalties or restrictions.,An action is considered a Significant Enforcement Action if `penimp` is 'Fine' or 'Ban' OR `acttake` is 'Suspension' OR `busrestr` is 'Full'.,domain_knowledge,"[38, 39]"
556,insider,20,Trader Position Holding Style,"Illustrates the typical duration traders hold their positions, based on their strategy.","`posspan`: 'Intraday' implies positions are typically opened and closed within the same trading day. 'Swing' suggests holding for a few days to weeks, capturing short-term price moves. 'Position' implies holding for weeks to months, based on broader trends. 'Long-term' indicates holding for months or years, often based on fundamental analysis.",value_illustration,-1
557,insider,21,Dark Pool Usage Venues,Explains the nature of dark pool usage indicated in transaction records.,"`darkusage`: This field lists Alternative Trading Systems (ATS) or other dark pools used. 'ATS-X', 'ATS-Y' are anonymized identifiers for specific dark pools, which are private exchanges where large orders can be executed without revealing intent to the public market, potentially reducing market impact. Usage patterns can be analyzed for regulatory compliance (e.g., ensuring best execution) or signs of avoiding market transparency.",value_illustration,-1
558,insider,22,Off-Market Trading Activity,Illustrates types of trading activity occurring outside public exchanges.,"`offmkt`: Describes trades not executed on lit exchanges. 'Internal crosses' specifically refer to a broker matching buy and sell orders from their own clients internally, without routing them to a public exchange. This can be efficient but requires monitoring to ensure fairness and prevent potential conflicts of interest.",value_illustration,-1
559,insider,23,Order Type Distribution,Illustrates the mix of primary order types used by a trader.,"`ordertypedist`: 'Market' indicates primarily using market orders (execute immediately at the best available price, prioritizing speed over price). 'Limit' indicates primarily using limit orders (execute only at a specified price or better, prioritizing price over speed). 'Mixed' suggests a combination of order types, reflecting varied trading strategies or objectives.",value_illustration,-1
560,insider,24,Momentum Ignition Signals,Explains the signals related to attempting to artificially create price momentum.,"`risk_indicators.momentignit`: 'Strong' indicates patterns consistent with attempts to trigger momentum algorithms or attract other traders by creating a false sense of rapid price movement (e.g., through rapid successive trades). 'Weak' suggests such patterns are less evident or absent. This is a potential indicator of manipulative behavior.",value_illustration,-1
561,insider,25,Marking the Close Patterns,Explains the patterns associated with influencing the closing price of a security.,"`risk_indicators.markclosepat`: 'Frequent' indicates repeated trading activity near the market close, potentially intended to manipulate the closing price (e.g., to affect margin calls, NAV calculations, or settlement prices). 'Occasional' suggests such activity is infrequent or less patterned. Marking the close is a prohibited manipulative practice.",value_illustration,-1
562,insider,26,Unusual Option Activity Level,Illustrates the degree of detected unusual options trading volume or types.,"`unuoptact`: 'High' suggests significant deviations from normal option trading patterns in terms of volume, strike prices, or expiration dates, potentially indicating informed trading or speculation ahead of news. 'Moderate' indicates some unusual activity, but less pronounced. This can be a flag for investigating potential insider information.",value_illustration,-1
563,insider,27,Information Leakage Score Interpretation,"Provides context for the information leakage score, indicating potential trading on non-public information.","`infoleaksc`: A score typically from 0-100. Low scores (<20) suggest little evidence of trading activity ahead of significant news or events. Moderate scores (20-50) warrant attention and may correlate with known events. High scores (>50) strongly suggest potential trading based on material non-public information, requiring investigation.",value_illustration,-1
564,insider,28,Pattern Similarity Score Context,"Provides context for the pattern similarity score, comparing trading to known illicit behaviors.","`patsim`: A score typically ranging from 0 to 1. Values close to 1 indicate a high similarity to known illicit trading patterns cataloged by the surveillance system (e.g., layering, spoofing, wash trading). Values close to 0 indicate the observed trading patterns do not strongly match known manipulative techniques.",value_illustration,-1
565,insider,29,Trading Restriction Period Types,Explains the types of trading restrictions imposed as part of enforcement.,"`traderestr`: 'Blackout' typically refers to a complete prohibition on trading specific securities or during certain periods (e.g., around earnings announcements). 'Special' indicates other, specific restrictions tailored to the case, which might include limits on order types, position sizes, or requiring pre-trade approvals.",value_illustration,-1
566,insider,30,Risk-Adjusted Turnover (RAT),Calculates trader turnover scaled by their leverage exposure.,RAT = \text{DTR} \times \text{TLE} \\ \text{where DTR is Daily Turnover Rate  and TLE is Trader Leverage Exposure .},calculation_knowledge,"[0, 2]"
567,insider,31,Combined Manipulation Indicator (CMI),A combined score reflecting both general suspicious activity and specific pattern anomalies.,CMI = (\text{SAI} + \text{PAS}) / 2 \\ \text{where SAI is Suspicious Activity Index  and PAS is Pattern Anomaly Score .},calculation_knowledge,"[3, 4]"
568,insider,32,Compliance Health Score (CHS),"Inverse score reflecting compliance history severity, penalizing high recidivism and poor ratings.",CHS = \frac{1}{1 + \text{CRS} \times \text{ComplianceRatingValue}},calculation_knowledge,"[5, 71]"
569,insider,33,Weighted Investigation Score (WIS),Combines raw investigation scores with the current alert severity level.,"WIS = \text{III} \times \text{AlertLevelMultiplier} \\ \text{where III is Investigation Intensity Index } \\ \text{and AlertLevelMultiplier maps Alert Level Severity  to numeric: 'Low'=1, 'Medium'=2, 'High'=3, 'Critical'=4.}",calculation_knowledge,"[6, 35]"
570,insider,34,Sentiment-Weighted Option Volume (SWOV),Adjusts the option volume ratio based on the divergence between news and social sentiment.,SWOV = \text{optvolrt} \times (1 + \text{SDF}) \\ \text{where SDF is the Sentiment Divergence Factor .},calculation_knowledge,[7]
571,insider,35,Logarithmic Enforcement Fine Impact (LEFI),"Calculates the log-scaled financial impact ratio of enforcement fines, emphasizing order of magnitude.","LEFI = \text{EFIR} \times \log_{10}(\text{Max}(10, \text{penamt})) \\ \text{where EFIR is the Enforcement Financial Impact Ratio .}",calculation_knowledge,[9]
572,insider,36,Aggressive Trading Intensity (ATI),"Measures intensity by combining high turnover, leverage, and order modification frequency.",ATI = \text{DTR} \times \text{TLE} \times \text{OMI}.,calculation_knowledge,"[0, 1, 2]"
573,insider,37,Suspicion-Weighted Turnover (SWT),Calculates daily turnover weighted by the Suspicious Activity Index.,SWT = \text{SAI} \times \text{DTR} \\ \text{where SAI is Suspicious Activity Index  and DTR is Daily Turnover Rate .},calculation_knowledge,"[0, 3]"
574,insider,38,Boosted Insider Leakage Score (BILS),Increases the Information Leakage Score if a Potential Insider Trading Flag is also present.,BILS = \text{InfoLeakageScoreValue} \times (1.5 \text{ if Potential Insider Trading Flag  is True else } 1.0) \\ \text{where InfoLeakageScoreValue is from Information Leakage Score Interpretation .},calculation_knowledge,"[11, 27]"
575,insider,39,Market-Adjusted Pattern Anomaly (MAPA),"Calculates pattern anomaly score adjusted for market correlation, highlighting non-market related deviations.",MAPA = \text{PAS} \times (1 - \text{mktcorr}) \\ \text{where PAS is Pattern Anomaly Score .},calculation_knowledge,[4]
576,insider,40,High-Frequency High-Risk Trader,Identifies traders classified as High-Risk who also operate at high frequency.,A trader matching the High-Risk Trader Profile  AND whose freqscope is 'High'.,domain_knowledge,[10]
577,insider,41,Suspected Event-Driven Insider,Flags traders identified as event-driven who also trigger potential insider trading alerts.,A trader who meets the criteria for Event-Driven Trader  AND for whom the Potential Insider Trading Flag  is True.,domain_knowledge,"[11, 17]"
578,insider,42,Confirmed Manipulator Under Scrutiny,Identifies traders with confirmed manipulative patterns whose cases are under high scrutiny.,A trader exhibiting a confirmed Market Manipulation Pattern: Layering/Spoofing  AND whose case status is Elevated Regulatory Scrutiny .,domain_knowledge,"[12, 14]"
579,insider,43,High-Risk Collusion Group Member,Identifies traders within a suspected collusion network who individually exhibit high-risk behavior.,A trader flagged by the Collusion Network Indicator  AND who also meets the High-Risk Trader Profile  criteria.,domain_knowledge,"[10, 13]"
580,insider,44,Chronic Compliance Violator,Identifies traders with a problematic history and a high recidivism score.,A trader identified as having a Problematic Compliance History  AND whose Compliance Recidivism Score (CRS)  is greater than 1.5.,domain_knowledge,"[5, 15]"
581,insider,45,High-Volume Wash Trading Concern,Flags traders with wash trading alerts who also trade significant volume.,A trader triggering a Wash Trading Alert  AND whose voldaily exceeds 1000000.,domain_knowledge,[16]
582,insider,46,Aggressive Event Speculator,Classifies event-driven traders who employ an aggressive risk strategy.,A trader classified as an Event-Driven Trader  AND whose Trader Risk Appetite  is 'Aggressive'.,domain_knowledge,"[17, 23]"
583,insider,47,Potentially Evasive Order Modifier,Flags high cancellation/modification traders who make significant use of dark pools.,A trader identified as a High Cancellation/Modification Trader  AND whose transaction records show Dark Pool Usage  in more than 50% of instances.,domain_knowledge,"[18, 21]"
584,insider,48,Financially Impactful Enforcement Case,Identifies traders who faced significant enforcement actions with a high financial impact relative to their account size.,A trader subject to a Significant Enforcement Action  AND whose Enforcement Financial Impact Ratio (EFIR)  is greater than 0.1.,domain_knowledge,"[9, 19]"
585,insider,49,Peer Mimicry Suspicion,"Flags traders whose behavior closely matches peers but deviates little from known patterns, potentially mimicking a risky group.","A trader with a low Pattern Anomaly Score (PAS)  (e.g., < 0.1) BUT a high peercorr (e.g., > 0.7), suggesting potential mimicry rather than independent strategy, possibly following a group engaged in problematic behavior.",domain_knowledge,[4]
586,insider,50,Investigation Compliance Risk Index (ICRI),"Combines the weighted investigation score with the inverse compliance health score, highlighting cases that are both problematic and under intense investigation.",ICRI = \text{WIS} \times (1 - \text{CHS}) \\ \text{where WIS is Weighted Investigation Score  and CHS is Compliance Health Score .},calculation_knowledge,"[32, 33]"
587,insider,51,Sentiment-Driven Leakage Risk (SDLR),Calculates potential information leakage risk weighted by sentiment-driven unusual option volume.,SDLR = \text{SWOV} \times \text{infoleaksc}.,calculation_knowledge,"[27, 34]"
588,insider,52,Unique Pattern Deviation Ratio (UPDR),"Measures the ratio of unique pattern deviation (anomaly) to the similarity with known illicit patterns, indicating how unusual the potentially illicit behavior is.","UPDR = \frac{\text{PAS}}{\text{Max}(0.01, \text{patsim})}",calculation_knowledge,"[4, 28]"
589,insider,53,Recidivism Enforcement Severity (RES),"Multiplies the compliance recidivism score by the enforcement financial impact, highlighting costly repeat offenders.",RES = \text{CRS} \times \text{EFIR} \\ \text{where CRS is Compliance Recidivism Score  and EFIR is Enforcement Financial Impact Ratio .},calculation_knowledge,"[5, 9]"
590,insider,54,Aggressive Suspicion Score (ASS),"Combines overall suspicious activity index with aggressive trading intensity, identifying traders who are both suspicious and trade aggressively.",ASS = \text{SAI} \times \text{ATI},calculation_knowledge,"[3, 36]"
591,insider,55,Capital-Adjusted Investigation Intensity (CAII),"Normalizes the investigation intensity index by the trader's account balance, showing investigation focus relative to trader size.","CAII = \frac{\text{III}}{\text{Max}(1000, \text{acctbal})} \\ \text{where III is Investigation Intensity Index .}",calculation_knowledge,[6]
592,insider,56,Market-Agnostic Suspicion Index (MASI),"Combines the general suspicion index with market-adjusted pattern anomaly, focusing on suspicious activity independent of market moves.",MASI = (\text{SAI} + \text{MAPA}) / 2 \\ \text{where SAI is Suspicious Activity Index  and MAPA is Market-Adjusted Pattern Anomaly .},calculation_knowledge,"[3, 39]"
593,insider,57,Cross-Modification Ratio (CMR),"Calculates the ratio of cross-trade frequency to order modification intensity, potentially indicating coordinated or manipulative crossing activity.","CMR = \frac{\text{crossfreq}}{\text{Max}(0.01, \text{OMI})} \\ \text{where OMI is Order Modification Intensity .}",calculation_knowledge,[1]
594,insider,58,Insider Sentiment Short Ratio (ISSR),"Combines boosted insider leakage score with relative short interest, identifying potential insider trading concurrent with high relative short interest.",ISSR = \text{BILS} \times \text{RSI} \\ \text{where BILS is Boosted Insider Leakage Score  and RSI is Relative Short Interest .},calculation_knowledge,"[8, 38]"
595,insider,59,Risk-Adjusted Win Rate (RAWR),Calculates the trader's historical win percentage adjusted for their leverage exposure.,"RAWR = \frac{\text{trading_performance.winpct}}{\text{Max}(1, \text{TLE})}",calculation_knowledge,[2]
596,insider,60,High-Risk Manipulator Candidate,Identifies traders flagged for both high-risk profiles and specific market manipulation patterns.,A trader who meets the High-Risk Trader Profile  AND is flagged for Market Manipulation Pattern: Layering/Spoofing .,domain_knowledge,"[10, 12]"
597,insider,61,Escalated Compliance Failure,Identifies traders with a problematic compliance history who have now incurred significant enforcement actions.,A trader identified with Problematic Compliance History  AND subject to a Significant Enforcement Action .,domain_knowledge,"[15, 19]"
598,insider,62,Networked Mimicry Risk,Flags traders suspected of peer mimicry who are also part of an identified potential collusion network.,A trader flagged for Peer Mimicry Suspicion  AND associated with a Collusion Network Indicator .,domain_knowledge,"[13, 49]"
599,insider,63,High-Scrutiny Wash Trading Case,Identifies compliance cases involving high-volume wash trading concerns that are also under elevated regulatory scrutiny.,A compliance case flagged for Elevated Regulatory Scrutiny  AND linked to a High-Volume Wash Trading Concern .,domain_knowledge,"[14, 45]"
600,insider,64,Volatile Event Speculator,"Flags aggressive event speculators whose trading coincides with high sentiment divergence, indicating potential reaction to conflicting information.","A trader identified as an Aggressive Event Speculator  AND associated with a high Sentiment Divergence Factor (e.g., SDF > 1.0).",domain_knowledge,"[7, 46]"
601,insider,65,Confirmed Evasive Layering/Spoofing,"Identifies traders confirmed to be layering or spoofing who also exhibit high cancellation/modification behavior, suggesting deliberate evasion.",A trader flagged as a High Cancellation/Modification Trader  AND confirmed via Market Manipulation Pattern: Layering/Spoofing  where `risk_indicators.layerind` is 'Confirmed' or `risk_indicators.spoofprob` > 0.75.,domain_knowledge,"[12, 18]"
602,insider,66,High Velocity Suspicion Trader,Identifies traders exhibiting both high risk-adjusted turnover and a high suspicious activity index.,"A trader with a high Risk-Adjusted Turnover (RAT)  (e.g., > 1.0) AND a high Suspicious Activity Index (SAI)  (e.g., > 0.6).",domain_knowledge,"[3, 30]"
603,insider,67,High-Intensity Insider Investigation,"Flags investigations triggered by potential insider trading that show high intensity scores, suggesting significant findings.","An investigation linked to a Potential Insider Trading Flag  AND having a high Investigation Intensity Index (III)  (e.g., > 70).",domain_knowledge,"[6, 11]"
604,insider,68,Severe Chronic Violator Case,Identifies compliance cases under elevated scrutiny involving traders flagged as chronic compliance violators.,A compliance case flagged for Elevated Regulatory Scrutiny  AND involving a trader identified as a Chronic Compliance Violator .,domain_knowledge,"[14, 44]"
605,insider,69,Costly High-Frequency Risk Enforcement,"Identifies enforcement cases with significant financial impact against traders previously identified as high-frequency, high-risk.",An enforcement case identified as Financially Impactful  targeting a trader previously flagged as a High-Frequency High-Risk Trader .,domain_knowledge,"[40, 48]"
606,insider,70,High SDLR Transaction,Identifies transactions deemed high-risk based on their Sentiment-Driven Leakage Risk score exceeding a specific threshold.,A transaction where the calculated Sentiment-Driven Leakage Risk (SDLR) > 1000.,domain_knowledge,[51]
607,insider,71,Compliance Rating Grade,Explains the overall compliance assessment grade assigned in compliance cases.,`comprate`: The compliance rating grade. 'A' represents excellent compliance. 'B' indicates good compliance with minor issues. 'C' suggests significant compliance deficiencies needing attention. 'D' signifies serious or repeated compliance failures requiring immediate action.,value_illustration,-1
608,insider,72,Premature Resolution Block,"A business rule preventing an enforcement action from being marked as 'Resolved' if associated risk metrics (like III) exceed a predefined threshold, ensuring high-risk cases receive sufficient review.",Block UPDATE of enforcementactions.resstat to 'Resolved' IF linked investigationdetails yield an Investigation Intensity Index > 75,domain_knowledge,[6]
609,insider,73,Peer Correlation Z-Score,A normalized score indicating how many standard deviations an individual record's peer correlation ('peercorr') is away from the average peer correlation of all traders within the same trader kind ('tradekind'). Used for standardized comparison across different peer groups.,"Z = (peercorr - AVG(peercorr) OVER (PARTITION BY tradekind)) / STDDEV_SAMP(peercorr) OVER (PARTITION BY tradekind), with Z=0 if STDDEV_SAMP is 0 or NULL.",calculation_knowledge,-1
610,vaccine,0,Temperature Stability Score (TSS),Calculates the overall temperature stability of a container based on deviations and critical events.,TSS = (1 - \frac{TempDevCount}{100}) \times (1 - \frac{CritEvents}{10}) \times TempStabIdx,calculation_knowledge,-1
611,vaccine,1,Coolant Depletion Rate (CDR),Measures how quickly the coolant is being depleted.,CDR = \frac{100 - CoolRemainPct}{(Current_Date - RefillLatest)},calculation_knowledge,-1
612,vaccine,2,Container Risk Index (CRI),Calculates overall risk level for a container based on temperature stability and coolant status.,CRI = (1 - TSS) \times (1 - \frac{CoolRemainPct}{100}),calculation_knowledge,[0]
613,vaccine,3,Vaccine Viability Period (VVP),Calculates remaining viable days for vaccines considering temperature deviations.,VVP = (ExpireDay - Current_Date) \times TSS,calculation_knowledge,[0]
614,vaccine,4,Route Completion Percentage (RCP),Calculates the percentage of route completed.,RCP = \frac{DistDoneKm}{DistDoneKm + DistLeftKm} \times 100,calculation_knowledge,-1
615,vaccine,5,Storage Efficiency Ratio (SER),Measures how efficiently container volume is utilized.,SER = \frac{VialTally \times 10}{VolLiters},calculation_knowledge,-1
616,vaccine,6,Logger Health Index (LHI),Overall health score for data logger.,LHI = \frac{BatteryPct}{100} \times (1 - \frac{MemUsePct}{100}) \times \frac{DataPct}{100},calculation_knowledge,-1
617,vaccine,7,Maintenance Compliance Score (MCS),Calculates compliance with maintenance schedules.,MCS = CompScore \times (1 - \frac{Incidents}{10}),calculation_knowledge,-1
618,vaccine,8,Handling Quality Index (HQI),Measures quality of shipment handling.,HQI = (1 - \frac{HandleEvents}{100}) \times (1 - \frac{CritEvents}{10}),calculation_knowledge,-1
619,vaccine,9,Temperature Breach Severity (TBS),Calculates severity of temperature breaches.,TBS = \frac{|TempNowC - StoreTempC|}{TempTolC} \times TempDevCount,calculation_knowledge,-1
620,vaccine,10,Container Health Status,Classifies containers based on overall risk and temperature stability to prioritize immediate action and monitor operational trends.,"Four-level classification based on CRI, TSS, and TBS:
- Critical: CRI > 0.6 OR current TSS < 0.4
- Unstable: Average TSS < 0.4 OR maximum TBS > 1.5 over the past 1 year, and not Critical
- Moderate: Average TSS >= 0.4 AND maximum TBS <= 1.5 over the past 1 year, and not Critical
- Stable: Average TSS >= 0.7 AND maximum TBS <= 1.0 over the past 1 year, and not Critical",domain_knowledge,"[0, 2, 9]"
621,vaccine,11,Compromised Shipment,Identifies shipments with serious integrity issues.,A shipment with IntegrityMark='Compromised' or SealFlag='Broken' or TamperSign='Confirmed',domain_knowledge,-1
622,vaccine,12,High-Risk Route,Identifies high-risk transportation routes.,A route where RCP < 50% and CRI > 0.4,domain_knowledge,"[2, 4]"
623,vaccine,13,Maintenance Due,Identifies equipment requiring maintenance.,Equipment where MCS < 0.7 and (Current_Date > MaintDateNext or Current_Date > CalibDateNext),domain_knowledge,[7]
624,vaccine,14,Stable Transport,Identifies stable transport conditions.,Transport where HQI > 0.9 and TSS > 0.8,domain_knowledge,"[0, 8]"
625,vaccine,15,Logger Failure Risk,Identifies loggers at risk of failure.,A logger where LHI < 0.3 or (BatteryPct < 20 and PwrBackupFlag='Not Available'),domain_knowledge,[6]
626,vaccine,16,Temperature Alert,Identifies critical temperature conditions.,A condition where TBS > 2.0 and TempDevCount > 5,domain_knowledge,[9]
627,vaccine,17,Coolant Critical,Identifies critical coolant conditions.,A condition where CDR > 1 and CoolRemainPct < 30,domain_knowledge,[1]
628,vaccine,18,Efficient Container,Identifies efficiently utilized containers.,A container where SER > 0.8 and TSS > 0.9,domain_knowledge,"[0, 5]"
629,vaccine,19,Quality Compromise,Identifies quality-compromised shipments.,A shipment where VVP < 30 or QualCheck='Failed',domain_knowledge,[3]
630,vaccine,20,TempNowC Value,Illustrates safe temperature range,Temperature between 2°C and 8°C indicates optimal vaccine storage conditions.,value_illustration,-1
631,vaccine,21,CompScore Value,Illustrates excellent compliance level,A score above 0.9 indicates excellent compliance with all regulatory requirements.,value_illustration,-1
632,vaccine,22,CommProto: 'RF',Illustrates RF communication protocol,Uses 433MHz ISM band radio frequency transmission with FSK modulation for short-range data exchange below 100m.,value_illustration,-1
633,vaccine,23,TransmitFlag: 'Real-time',Illustrates immediate data transmission,Indicates data is being transmitted to the central system without delay as it is collected.,value_illustration,-1
634,vaccine,24,CoolKind: 'Phase Change Material',Illustrates PCM cooling mechanism,Uses paraffin-based material with 2-8°C phase transition point and 200 kJ/kg latent heat capacity for temperature stabilization.,value_illustration,-1
635,vaccine,25,VehicleKind: 'Reefer Container',Illustrates specialized container type,Self-powered refrigeration unit maintaining -20°C to +30°C with R-134a refrigerant and independent power supply lasting 72 hours.,value_illustration,-1
636,vaccine,26,CommProto: 'Satellite',Illustrates satellite communication mode,Indicates the system is using satellite networks for global coverage and reliable data transmission.,value_illustration,-1
637,vaccine,27,VacVariant: 'Viral Vector',Illustrates vaccine technology type,"Uses modified adenovirus Ad26 vector expressing SARS-CoV-2 spike protein, requiring -20°C storage with 6-month shelf life.",value_illustration,-1
638,vaccine,28,GPSFlag: 'Active',Illustrates active location tracking,Indicates that GPS tracking is operational and accurately reporting location data.,value_illustration,-1
639,vaccine,29,LightLux Value,Illustrates light exposure meaning.,"Measured in lux. 0-10: Dark (ideal), 10-50: Low light, >50: Excessive exposure risk.",value_illustration,-1
640,vaccine,30,Total Risk Score (TRS),Combines container risk and handling quality for overall risk assessment.,TRS = \text{CRI} \times (1 - \text{HQI}) \times (1 + \text{TBS}),calculation_knowledge,"[2, 8, 9]"
641,vaccine,31,Coolant Efficiency Index (CEI),Measures how efficiently coolant is maintaining temperature stability.,CEI = \text{TSS} \times \frac{\text{CoolRemainPct}}{100} \times (1 - \frac{\text{CDR}}{20}),calculation_knowledge,"[0, 1]"
642,vaccine,32,Combined Maintenance Risk (CMR),Evaluates overall maintenance risk considering compliance and incidents.,CMR = (1 - \text{MCS}) \times (1 + \frac{\text{TBS}}{5}) \times (1 - \text{LHI}),calculation_knowledge,"[7, 9, 6]"
643,vaccine,33,Shipment Quality Index (SQI),Overall quality score for shipment considering multiple factors.,SQI = \frac{\text{VVP}}{365} \times \text{HQI} \times (1 - \text{CRI}),calculation_knowledge,"[3, 8, 2]"
644,vaccine,34,Logger Reliability Score (LRS),Comprehensive score for logger reliability.,LRS = 	ext{LHI} \times (1 - \text{CMR}) \times \text{TSS},calculation_knowledge,"[6, 32, 0]"
645,vaccine,35,Transport Safety Rating (TSR),Safety rating for transport considering multiple factors.,TSR = \frac{\text{RCP}}{100} \times (1 - \text{TRS}) \times \text{HQI},calculation_knowledge,"[4, 30, 8]"
646,vaccine,36,Container Efficiency Score (CES),Overall container efficiency considering multiple metrics.,CES = \text{SER} \times \text{CEI} \times (1 - \text{CRI}),calculation_knowledge,"[5, 31, 2]"
647,vaccine,37,Quality Maintenance Index (QMI),Combined quality and maintenance metric.,QMI = \text{MCS} \times \text{SQI} \times (1 - \frac{\text{TBS}}{10}),calculation_knowledge,"[7, 33, 9]"
648,vaccine,38,Route Risk Factor (RRF),Comprehensive route risk assessment.,RRF = (1 - \frac{	ext{RCP}}{100}) \times \text{TRS} \times (1 - \text{CEI}),calculation_knowledge,"[4, 30, 31]"
649,vaccine,39,Vaccine Safety Index (VSI),Overall safety index for vaccine shipment.,VSI = \frac{	ext{VVP}}{365} \times \text{CEI} \times (1 - \text{TRS}),calculation_knowledge,"[3, 31, 30]"
650,vaccine,40,Critical Transport Condition,Identifies critically unsafe transport conditions.,A transport condition where TSR < 0.4 and TRS > 0.6,domain_knowledge,"[35, 30]"
651,vaccine,41,Severe Container Risk,Identifies containers with severe combined risks.,A container where CES < 0.3 and CEI < 0.5,domain_knowledge,"[36, 31]"
652,vaccine,42,High Maintenance Priority,Identifies equipment requiring urgent maintenance.,Equipment where CMR > 0.7 and QMI < 0.4,domain_knowledge,"[32, 37]"
653,vaccine,43,Critical Route Status,Identifies routes with critical risk levels.,A route where RRF > 0.8 and TSR < 0.3,domain_knowledge,"[38, 35]"
654,vaccine,44,Unsafe Vaccine Condition,Identifies potentially compromised vaccines.,A condition where VSI < 0.4 and CES < 0.5,domain_knowledge,"[39, 36]"
655,vaccine,45,Logger Critical State,Identifies critically failing loggers.,A logger where LRS < 0.3 and CMR > 0.8,domain_knowledge,"[34, 32]"
656,vaccine,46,Quality Alert Status,Identifies severe quality issues.,A status where QMI < 0.3 and VSI < 0.5,domain_knowledge,"[37, 39]"
657,vaccine,47,Transport Safety Alert,Identifies unsafe transport conditions.,A condition where TSR < 0.5 and RRF > 0.7,domain_knowledge,"[35, 38]"
658,vaccine,48,Container Alert Status,Identifies containers requiring urgent attention.,A status where CES < 0.4 and TRS > 0.7,domain_knowledge,"[36, 30]"
659,vaccine,49,Critical Safety Condition,Identifies critically unsafe overall conditions.,A condition where VSI < 0.3 and TSR < 0.4,domain_knowledge,"[39, 35]"
660,vaccine,50,Thermal Stability Coefficient (TSC),Advanced temperature stability metric incorporating thermal mass and ambient conditions.,TSC = \text{TSS} \cdot e^{-\frac{|\text{TempNowC} - \text{StoreTempC}|}{5}} \cdot \left(1 - \alpha \cdot \frac{\text{TempNowC} - \text{TempPrevC}}{\text{ReadingInterval}}\right),calculation_knowledge,"[0, 60]"
661,vaccine,51,Multi-Parameter Risk Assessment (MPRA),Comprehensive risk evaluation using multiple environmental parameters.,MPRA = \sqrt{\text{CRI}^2 + \text{TBS}^2 + (1-\text{HQI})^2} \times (1 + \frac{\text{CDR}}{\text{CDR}_{\text{max}}}),calculation_knowledge,"[2, 9, 8, 1]"
662,vaccine,52,Time-Weighted Quality Decay (TWQD),Calculates quality deterioration rate considering time and environmental factors.,TWQD = -\frac{d}{dt}(\text{VVP}) \times (1 + \beta\text{TBS}) \times (1 + \gamma(\text{1-TSS})),calculation_knowledge,"[3, 9, 0]"
663,vaccine,53,Environmental Stress Factor (ESF),Quantifies combined environmental stressors on vaccine stability.,ESF = \text{TSS} \cdot (1 + \frac{\text{TBS}}{5}) \cdot \text{CEI} \cdot (1 + \frac{|\text{TempNowC} - \text{StoreTempC}|}{\text{TempTolC}}),calculation_knowledge,"[0, 9, 31]"
664,vaccine,54,Logistics Performance Metric (LPM),Evaluates overall logistics efficiency with temporal considerations.,LPM = \text{RCP} \cdot \frac{\text{HQI}}{\sqrt{1 + \text{TRS}}},calculation_knowledge,"[4, 8, 30]"
665,vaccine,55,Critical Cascade Condition,Identifies systemic failure patterns in the cold chain.,A condition where (MPRA > 0.8 AND TSC < 0.4) OR (TWQD > 0.6 AND ESF > 0.7),domain_knowledge,"[51, 50, 52, 53]"
666,vaccine,56,Compound Quality Risk,Identifies complex quality degradation scenarios.,A status where VSI decreases over three consecutive readings AND LPM < 0.5 AND ESF > 0.6,domain_knowledge,"[39, 54, 53]"
667,vaccine,57,Dynamic Stability Threshold,Evaluates stability under varying conditions.,A condition where average TSC over last 5 readings < 0.7 AND MPRA > 0.6,domain_knowledge,"[50, 51]"
668,vaccine,58,Multi-System Failure Risk,Identifies concurrent failures across multiple subsystems.,"A state where (CRI + TBS + (1-HQI))/3 > 0.7 AND all of (TSC, LPM, ESF) < 0.3",domain_knowledge,"[2, 8, 9, 50, 54, 53]"
669,vaccine,59,Predictive Degradation Alert,Forecasts potential quality degradation based on current trends.,Alert when TWQD increases over 3 consecutive readings AND ESF > 0.5 AND TempDevCount > 3,domain_knowledge,"[52, 53]"
670,vaccine,60,ReadingInterval,Time denominator used for calculating rate of temperature change,"Time interval in minutes between successive temperature readings, used to normalize temperature differences into rates of change. Standard value is 15 minutes with acceptable range 5-60 minutes depending on monitoring requirements",domain_knowledge,-1
671,vaccine,61,Days Overdue,Calculates the number of days past the scheduled maintenance or calibration date.,"DaysOverdue = \max\left( (\text{Current_Date} - \text{MaintDateNext}), (\text{Current_Date} - \text{CalibDateNext}), 0 \right)",calculation_knowledge,-1
672,vaccine,62,Depletion Rank,Ranks containers based on the rate of coolant depletion.,Rank assigned to containers based on descending order of CDR,domain_knowledge,[1]
673,vaccine,63,Efficiency Rank,Ranks containers based on their storage efficiency.,Rank assigned to containers based on descending order of SER,domain_knowledge,[5]
674,vaccine,64,Risk Rank,Ranks containers based on their risk level.,Rank assigned to containers based on descending order of CRI,domain_knowledge,[2]
675,vaccine,65,Urgency Rank,Ranks vehicles based on maintenance risk and overdue days.,\text{Rank assigned to vehicles based on descending order of } \text{CMR} + \text{DaysOverdue} / 30,calculation_knowledge,"[32, 61]"
676,mental,0,Average PHQ-9 Score by Facility (APSF),Calculates the average PHQ-9 depression score for patients assessed at a specific facility.,APSF = \frac{\sum_{i \in assessmentsymptomsandrisk} (mental\_health\_scores_i['depression']['phq9\_score'])} {|assessmentsymptomsandrisk|},calculation_knowledge,-1
677,mental,1,Average GAD-7 Score by Facility (AGSF),Calculates the average GAD-7 anxiety score for patients assessed at a specific facility.,"AGSF = \frac{\sum_{i \in assessmentsymptomsandrisk} (mental\_health\_scores_i['anxiety']['gad7\_score'])} {|assessmentsymptomsandrisk|}, \text{where } mental\_health\_scores_i['anxiety']['gad7\_score'] \text{ is the GAD-7 score for each assessment in the assessmentsymptomsandrisk table, linked to a facility via encounters.facid}",calculation_knowledge,-1
678,mental,2,Treatment Adherence Rate (TAR),Measures the proportion of patients with high or medium treatment adherence at a facility.,"TAR = \frac{|treatmentoutcomes \text{ with } txadh \in \{High, Medium\}|} {|treatmentoutcomes|}",calculation_knowledge,-1
679,mental,3,Suicide Risk Prevalence (SRP),Calculates the percentage of assessments indicating high or severe suicide risk at a facility.,"SRP = \frac{|assessmentsymptomsandrisk \text{ with } suicrisk \in \{High, Severe\}|} {|assessmentsymptomsandrisk|} \times 100",calculation_knowledge,-1
680,mental,4,Therapy Engagement Score (TES),Computes an average engagement score across therapy sessions.,"TES = \frac{\sum_{i \in treatmentbasics} (therapy\_details_i['engagement\_score'])} {|treatmentbasics|}, \text{where } engagement\_score = \begin{cases} 3 & \text{if } therapy\_details['engagement'] = High \\ 2 & \text{if } therapy\_details['engagement'] = Medium \\ 1 & \text{if } therapy\_details['engagement'] = Low \\ 0 & \text{if } therapy\_details['engagement'] = Non-compliant \end{cases}, \text{and } therapy\_details \text{ is the JSONB column in the treatmentbasics table}",calculation_knowledge,-1
681,mental,5,Facility Resource Adequacy Index (FRAI),Quantifies the adequacy of community resources available at a facility.,"FRAI = \frac{\sum_{i \in facilities} resource\_score_i} {|facilities|}, \text{where } resource\_score = \begin{cases} 3 & \text{if } support\_and\_resources['community\_resources'] = Comprehensive \\ 2 & \text{if } support\_and\_resources['community\_resources'] = Adequate \\ 1 & \text{if } support\_and\_resources['community\_resources'] = Limited \end{cases}, \text{and } support\_and\_resources \text{ is the JSONB column in the facilities table}",calculation_knowledge,-1
682,mental,6,Patient Functional Impairment Score (PFIS),Calculates an average functional impairment score across patients.,"PFIS = \frac{\sum_{i \in assessmentsocialanddiagnosis} funcimp\_score_i} {|assessmentsocialanddiagnosis|}, \text{where } funcimp\_score = \begin{cases} 3 & \text{if } funcimp = Severe \\ 2 & \text{if } funcimp = Moderate \\ 1 & \text{if } funcimp = Mild \end{cases}",calculation_knowledge,-1
683,mental,7,Crisis Intervention Frequency (CIF),Measures the average number of crisis interventions per patient at a facility.,CIF = \frac{\sum_{i \in treatmentbasics} crisisint_i} {|patients|},calculation_knowledge,-1
684,mental,8,Social Support Effectiveness (SSE),Evaluates the effectiveness of social support based on social support level and relationship quality.,"SSE = \frac{\sum_{i \in assessmentsocialanddiagnosis} (socsup\_score_i + relqual\_score_i)} {|assessmentsocialanddiagnosis|}, \text{where } socsup\_score = \begin{cases} 3 & \text{if } socsup = Strong \\ 2 & \text{if } socsup = Moderate \\ 1 & \text{if } socsup = Limited \end{cases}, \text{and } relqual\_score = \begin{cases} 3 & \text{if } relqual = Good \\ 2 & \text{if } relqual = Fair \\ 1 & \text{if } relqual = Poor \\ 0 & \text{if } relqual = Conflicted \end{cases}",calculation_knowledge,-1
685,mental,9,Missed Appointment Rate (MAR),Calculates the average number of missed appointments per patient at a facility.,MAR = \frac{\sum_{i \in encounters} missappt_i} {|patients|},calculation_knowledge,-1
686,mental,10,High-Risk Patient,Identifies patients with elevated suicide risk or severe symptoms.,"A patient with suicrisk \in \{High, Severe\} or mental\_health\_scores['depression']['phq9\_score'] > 15 or mental\_health\_scores['anxiety']['gad7\_score'] > 15,",domain_knowledge,-1
687,mental,11,Treatment-Resistant Patient,Identifies patients with poor treatment response despite adherence.,"A patient with txresp = Poor and txadh \in \{Medium, High\}",domain_knowledge,-1
688,mental,12,Complex Care Needs,Identifies patients requiring intensive care coordination due to multiple risk factors.,"A patient with SRP > 20\% and PFIS > 2.5 and subuse \in \{Opioids, Multiple\}",domain_knowledge,"[3, 6]"
689,mental,13,Stable Recovery Patient,Identifies patients showing stable recovery with good functional outcomes.,"A patient with recstatus = Stable and funcimpv \in \{Moderate, Significant\}",domain_knowledge,-1
690,mental,14,Low Engagement Risk,Identifies patients at risk of disengagement from therapy.,"A patient with TES < 1.5 and txeng \in \{Low, Non-compliant\}",domain_knowledge,[4]
691,mental,15,Resource-Supported Facility,Identifies facilities with adequate or comprehensive community resources.,A facility with resource_score \geq 2 \text{where } resource_score = \begin{cases} 3 & \text{if } support\_and\_resources['community\_resources'] = Comprehensive \\ 2 & \text{if } support\_and\_resources['community\_resources'] = Adequate \\ 1 & \text{if } support\_and\_resources['community\_resources'] = Limited \end{cases}.,domain_knowledge,[5]
692,mental,16,High Social Support Patient,Identifies patients with strong social support and good relationship quality.,A patient with SSE \geq 5,domain_knowledge,[8]
693,mental,17,Frequent Crisis Patient,Identifies patients with frequent crisis interventions.,A patient with CIF > 2,domain_knowledge,[7]
694,mental,18,Non-Compliant Patient,Identifies patients with consistent non-compliance in treatment.,A patient with txadh = Non-compliant and medadh = Non-compliant,domain_knowledge,-1
695,mental,19,High Appointment Adherence,Identifies patients with low missed appointment rates.,A patient with MAR < 1,domain_knowledge,[9]
696,mental,20,PHQ-9 Score (Depression),Illustrates the value of the PHQ-9 score for depression severity.,"Ranges from 0 to 27. A score of 0–4 indicates minimal depression, 5–9 mild, 10–14 moderate, 15–19 moderately severe, and 20–27 severe.",value_illustration,-1
697,mental,21,GAD-7 Score (Anxiety),Illustrates the value of the GAD-7 score for anxiety severity.,"Ranges from 0 to 21. A score of 0–4 indicates minimal anxiety, 5–9 mild, 10–14 moderate, and 15–21 severe.",value_illustration,-1
698,mental,22,Suicide Risk Level,Illustrates the value of the suicide risk level.,"Ranges from Low to Severe. Low indicates minimal risk, Medium indicates some concern, High indicates immediate concern, and Severe indicates urgent intervention needed.",value_illustration,-1
699,mental,23,Therapy Engagement,Illustrates the value of therapy engagement levels.,"Ranges from Non-compliant to High. Non-compliant indicates no participation, Low indicates minimal participation, Medium indicates regular participation, and High indicates active engagement.",value_illustration,-1
700,mental,24,Community Resources,Illustrates the value of community resource availability.,"Ranges from Limited to Comprehensive. Limited indicates few or no resources, Adequate indicates sufficient resources, and Comprehensive indicates extensive resources.",value_illustration,-1
701,mental,25,Functional Impairment,Illustrates the value of functional impairment levels.,"Ranges from Mild to Severe. Mild indicates minimal impact on daily life, Moderate indicates noticeable impact, and Severe indicates significant disruption.",value_illustration,-1
702,mental,26,Treatment Adherence,Illustrates the value of treatment adherence levels.,"Ranges from Non-compliant to High. Non-compliant indicates no adherence, Low indicates occasional adherence, Medium indicates regular adherence, and High indicates consistent adherence.",value_illustration,-1
703,mental,27,Crisis Intervention Count,Illustrates the value of the crisis intervention count.,"A numeric value indicating the number of crisis interventions. A value of 0 indicates no interventions, while higher values (e.g., 3) indicate frequent interventions.",value_illustration,-1
704,mental,28,Social Support Level,Illustrates the value of social support levels.,"Ranges from Limited to Strong. Limited indicates minimal support, Moderate indicates some support, and Strong indicates robust support.",value_illustration,-1
705,mental,29,Missed Appointment Count,Illustrates the value of the missed appointment count.,"A numeric value indicating the number of missed appointments. A value of 0 indicates perfect attendance, while higher values (e.g., 5) indicate frequent absences.",value_illustration,-1
706,mental,30,Symptom Severity Index (SSI),"Calculates a combined average symptom severity score for a facility, based on depression and anxiety.","SSI = \frac{APSF + AGSF}{2}, \text{using Average PHQ-9 Score (APSF) and Average GAD-7 Score (AGSF)}",calculation_knowledge,"[0, 1]"
707,mental,31,Engagement-Adherence Score (EAS),Computes a composite score reflecting patient participation and adherence to treatment plans at a facility.,"EAS = \frac{TES + (TAR \times 3)}{2}, \text{normalizing Treatment Adherence Rate (TAR) to the Therapy Engagement Score (TES) scale (0-3)}",calculation_knowledge,"[4, 2]"
708,mental,32,Facility Risk Profile Index (FRPI),Generates an index indicating the overall risk level associated with the patient population at a facility.,"FRPI = (\frac{SRP}{100} \times 5) + PFIS, \text{weighting Suicide Risk Prevalence (SRP) and combining with Patient Functional Impairment Score (PFIS)}",calculation_knowledge,"[3, 6]"
709,mental,33,Patient Stability Metric (PSM),"Calculates an index reflecting patient stability, inversely related to crisis frequency and missed appointments.","PSM = \frac{1}{1 + CIF + MAR}, \text{where higher values indicate greater stability based on Crisis Intervention Frequency (CIF) and Missed Appointment Rate (MAR)}",calculation_knowledge,"[7, 9]"
710,mental,34,Resource-Demand Differential (RDD),Measures the potential gap between average patient functional needs and available facility resources.,"RDD = PFIS - FRAI, \text{comparing Patient Functional Impairment Score (PFIS) to Facility Resource Adequacy Index (FRAI)}",calculation_knowledge,"[6, 5]"
711,mental,35,Socio-Environmental Support Index (SESI),Computes a composite index reflecting the quality of the patient's social environment and the facility's resource context.,"SESI = \frac{SSE_{avg} + FRAI}{2}, \text{combining average Social Support Effectiveness (SSE) across patients with Facility Resource Adequacy Index (FRAI)}",calculation_knowledge,"[8, 5]"
712,mental,36,Adherence Effectiveness Ratio (AER),"Calculates a ratio comparing treatment adherence rate to the average functional impairment, suggesting potential treatment impact relative to need.","AER = \frac{TAR}{PFIS}, \text{using Treatment Adherence Rate (TAR) and Patient Functional Impairment Score (PFIS) (handle PFIS=0)}",calculation_knowledge,"[2, 6]"
713,mental,37,Engagement Deficit Index (EDI),"Quantifies the degree of patient disengagement, considering both therapy engagement scores and appointment attendance.","EDI = (3 - TES) \times (1 + MAR), \text{calculating the gap from maximum Therapy Engagement Score (TES) weighted by Missed Appointment Rate (MAR)}",calculation_knowledge,"[4, 9]"
714,mental,38,Comprehensive Facility Risk Score (CFRS),"A normalized index assessing overall facility risk based on combined depression severity, suicide risk prevalence, and functional impairment.","CFRS = \frac{APSF}{27} + \frac{SRP}{100} + \frac{PFIS}{3}, \text{normalizing Average PHQ-9 Score (APSF), Suicide Risk Prevalence (SRP), and Patient Functional Impairment Score (PFIS) to a 0-1 scale and summing}",calculation_knowledge,"[0, 3, 6]"
715,mental,39,Support System Pressure Index (SSPI),Index assessing the pressure on support systems based on crisis frequency relative to social support effectiveness.,"SSPI = \frac{CIF}{SSE_{avg} + 1}, \text{calculating Crisis Intervention Frequency (CIF) relative to average Social Support Effectiveness (SSE) (adding 1 to avoid division by zero)}",calculation_knowledge,"[7, 8]"
716,mental,40,"High-Need, Under-Resourced Facility",Identifies facilities facing significant aggregate patient risk without adequate community resources.,"A facility where FRPI > 4.5 and FRAI < 1.5, \text{indicating high Facility Risk Profile Index (FRPI) and low Facility Resource Adequacy Index (FRAI)}",domain_knowledge,"[32, 5]"
717,mental,41,Facility with Engaged but High-Impairment Population,Identifies facilities where the patient population is generally engaged and adherent but continues to struggle with high functional impairment.,"A facility where EAS > 2.0 and PFIS > 2.0, \text{showing high Engagement-Adherence Score (EAS) alongside high Patient Functional Impairment Score (PFIS)}",domain_knowledge,"[31, 6]"
718,mental,42,Patient with Strong Recovery Capital,Identifies patients demonstrating high social support effectiveness coupled with low functional impairment.,"A patient where SSE \geq 5 and their funcimp value corresponds to a score of 1 (Mild), potentially indicating strong basis for sustained recovery. Relates to concepts measured by SSE and PFIS.",domain_knowledge,"[8, 6]"
719,mental,43,Facility Attrition Risk Indicator,"Identifies facilities potentially experiencing high patient dropout, characterized by low engagement/adherence and high missed appointment rates.","A facility where EAS < 1.5 and MAR > 2.5, \text{based on low Engagement-Adherence Score (EAS) and high Missed Appointment Rate (MAR)}",domain_knowledge,"[31, 9]"
720,mental,44,Well-Resourced High-Support Environment,Identifies facilities that are well-resourced and serve a patient population with generally high levels of social support effectiveness.,"A facility where FRAI \geq 2.0 and the average SSE \geq 4.5, \text{indicating high Facility Resource Adequacy Index (FRAI) and high average Social Support Effectiveness (SSE)}",domain_knowledge,"[5, 8]"
721,mental,45,Patient with Severe Comorbid Distress Profile,"Identifies patients experiencing significant simultaneous distress across depression, anxiety, and functional domains.","A patient where mental_health_scores['depression']['phq9_score'] \geq 15 AND mental_health_scores['anxiety']['gad7_score'] \geq 15 AND funcimp = 'Severe'. \text{This profile relates to high individual contributions to APSF, AGSF, and PFIS}.",domain_knowledge,"[0, 1, 6]"
722,mental,46,Facility with Potential Treatment Inertia,"Identifies facilities where patients seem engaged in therapy (high TES) but struggle with overall treatment adherence (low TAR), suggesting potential systemic barriers or resistance.","A facility where TES > 2.2 and TAR < 0.6, \text{based on Therapy Engagement Score (TES) and Treatment Adherence Rate (TAR)}",domain_knowledge,"[4, 2]"
723,mental,47,Patient with High Crisis & Low Support Profile,Identifies patients characterized by frequent crisis interventions and weak social support systems.,A patient where their individual crisisint count (from treatmentbasics table) > 2 AND their individual SSE score (calculated from assessmentsocialanddiagnosis) < 3. \text{This profile relates to high contribution to CIF and low individual SSE}.,domain_knowledge,"[7, 8]"
724,mental,48,Facility Demonstrating Strong Patient Retention,Identifies facilities showing positive performance indicators related to high patient adherence and low missed appointment rates.,"A facility where TAR > 0.75 and MAR < 1.0, \text{based on Treatment Adherence Rate (TAR) and Missed Appointment Rate (MAR)}",domain_knowledge,"[2, 9]"
725,mental,49,"High Severity, High Risk Patient Group",Identifies patients presenting with both high symptom severity (depression/anxiety) and elevated suicide risk.,"A patient where (mental_health_scores['depression']['phq9_score'] > 19 OR mental_health_scores['anxiety']['gad7_score'] > 14) AND suicrisk IN {'High', 'Severe'}.",domain_knowledge,-1
726,mental,50,Clinical Improvement Potential Index (CIPI),"Calculates a ratio comparing patient engagement/adherence to overall symptom severity at a facility, suggesting potential responsiveness to intervention.","CIPI = \frac{EAS}{SSI + 1}, \text{using Engagement-Adherence Score (EAS) and Symptom Severity Index (SSI). Higher values suggest higher engagement relative to current symptom burden.}",calculation_knowledge,"[31, 30]"
727,mental,51,Facility Efficiency Index (FEI),Estimates facility efficiency by relating the achieved patient stability metric to the available facility resource adequacy.,"FEI = PSM \times FRAI, \text{multiplying Patient Stability Metric (PSM) by Facility Resource Adequacy Index (FRAI). Higher scores suggest better stability achieved per resource level.}",calculation_knowledge,"[33, 5]"
728,mental,52,Therapeutic Alliance & Engagement Score (TAES),Computes a combined score reflecting both the average clinician-reported therapeutic alliance and the calculated therapy engagement score for a facility.,"TAES = \frac{Average(theralliance\_score) + TES}{2}, \text{where } theralliance\_score = \begin{cases} 3 & \text{if } theralliance = Strong \\ 2 & \text{if } theralliance = Moderate \\ 1 & \text{if } theralliance = Weak \\ 0 & \text{if } theralliance = Poor \end{cases}, \text{averaged across treatmentoutcomes, combined with Therapy Engagement Score (TES)}.",calculation_knowledge,[4]
729,mental,53,Recovery Trajectory Index (RTI),Estimates the effectiveness of treatment adherence in achieving functional improvement at a facility.,"RTI = \frac{\sum_{i \in treatmentoutcomes} (funcimpv\_score_i)}{|treatmentoutcomes|} \times TAR, \text{where } funcimpv\_score = \begin{cases} 3 & \text{if } funcimpv = Significant \\ 2 & \text{if } funcimpv = Moderate \\ 1 & \text{if } funcimpv = Minimal \end{cases}",calculation_knowledge,[2]
730,mental,54,Crisis Adherence Ratio (CAR),"Calculates the ratio of crisis intervention frequency to the treatment adherence rate, indicating crises occurring per unit of adherence.","CAR = \frac{CIF}{TAR + 0.01}, \text{dividing Crisis Intervention Frequency (CIF) by Treatment Adherence Rate (TAR) (adjusted to prevent division by zero). Higher values indicate more crises relative to adherence levels.}",calculation_knowledge,"[7, 2]"
731,mental,55,Facility with High Clinical Leverage Potential,"Identifies facilities with a highly engaged and adherent patient population that still experiences significant symptom severity, suggesting readiness for potentially more intensive or alternative interventions.","A facility where EAS > 2.5 AND SSI > 15, \text{indicating high Engagement-Adherence Score (EAS) alongside a high Symptom Severity Index (SSI).}",domain_knowledge,"[31, 30]"
732,mental,56,Patient Exhibiting Fragile Stability,"Identifies patients currently classified as 'Stable Recovery' but who exhibit risk factors like frequent missed appointments or low social support, suggesting potential for destabilization.",A patient meeting Stable Recovery Patient criteria BUT having an average missappt > 2 across their encounters OR an individual SSE score < 3. \text{Combines Stable Recovery Patient status with risk factors related to MAR and SSE}.,domain_knowledge,"[13, 9, 8]"
733,mental,57,Resource-Intensive High-Risk Patient Cohort,Identifies patients requiring significant care coordination and intervention due to possessing characteristics of both Complex Care Needs and Frequent Crisis Patterns.,A patient meeting criteria for both Complex Care Needs AND Frequent Crisis Patient (17).,domain_knowledge,"[12, 17]"
734,mental,58,Facility with Potential Engagement-Outcome Disconnect,Identifies facilities where high therapy engagement scores do not seem to translate into expected functional improvements or recovery progression.,"A facility where TES > 2.0 AND RTI < 0.8, \text{indicating high Therapy Engagement Score (TES) but a low Recovery Trajectory Index (RTI).}",domain_knowledge,"[4, 53]"
735,mental,59,Systemically Stressed Facility Environment,"Identifies facilities potentially facing overwhelming systemic stress, characterized by a significant gap between patient needs and resources, compounded by high patient attrition risk.",A facility where RDD > 1.0 AND meeting criteria for Facility Attrition Risk Indicator. \text{Combines a high Resource-Demand Differential (RDD) with indicators of high attrition risk.},domain_knowledge,"[34, 43]"
736,mental,60,Correlation Between Resource Adequacy and Adherence (CRAA),Measures the correlation between individual facility resource adequacy scores and treatment adherence rates.,"CRAA = \text{CORR}(resource\_score, tar), \text{where } resource\_score \text{ is the facility's resource adequacy score, and } tar \text{ is the treatment adherence rate for the facility.}",calculation_knowledge,"[5, 2]"
737,mental,61,Facility Performance Quadrant (FPQ),Categorizes facilities into performance quadrants based on their Treatment Adherence Rate and Patient Stability Metric relative to median thresholds.,"A facility is assigned to one of four quadrants: 'High Adherence, High Stability' if TAR ≥ median_tar and PSM ≥ median_psm; 'High Adherence, Low Stability' if TAR ≥ median_tar and PSM < median_psm; 'Low Adherence, High Stability' if TAR < median_tar and PSM ≥ median_psm; 'Low Adherence, Low Stability' if TAR < median_tar and PSM < median_psm.",domain_knowledge,"[2, 33]"
738,mental,62,Stale Treatment Outcome Records,"Treatment outcome records associated with encounters that occurred before a specific time threshold (e.g., older than 60 days).","Records in the `treatmentoutcomes` table where the `timemark` of the linked encounter in the `encounters` table is older than a defined interval (e.g., 60 days).",domain_knowledge,-1
739,credit,0,Debt-to-Income Ratio (DTI),Calculates the proportion of a customer's monthly income that goes toward debt payments.,DTI = \frac{\text{Total Monthly Debt Payments}}{\text{Monthly Income}} = debincratio,calculation_knowledge,-1
740,credit,1,Credit Utilization Ratio (CUR),Measures how much of available credit a customer is currently using.,CUR = \frac{\text{Total Credit Used}}{\text{Total Credit Limit}} = credutil,calculation_knowledge,-1
741,credit,2,Loan-to-Value Ratio (LTV),Calculates the ratio of loan amount to the value of the asset securing the loan.,LTV = \frac{\text{Mortgage Balance}}{\text{Property Value}} = \frac{\text{propfinancialdata.mortgagebits.mortbalance}}{\text{propfinancialdata.propvalue}},calculation_knowledge,-1
742,credit,3,Customer Lifetime Value (CLV),Measures the total worth of a customer to the financial institution over the entire relationship.,"CLV = custlifeval, which factors in product usage, tenure, profitability, and expected future transactions.",calculation_knowledge,-1
743,credit,4,Net Worth,Calculates the financial value of a customer by subtracting liabilities from assets.,Net Worth = \text{Total Assets} - \text{Total Liabilities} = totassets - totliabs = networth,calculation_knowledge,-1
744,credit,5,Credit Health Score (CHS),Composite score measuring overall credit wellness based on multiple factors.,"CHS = 0.4 × \frac{credscore}{850} + 0.2 × (1 - credutil) + 0.2 × (1 - debincratio) + 0.1 × \frac{credageyrs}{20} + 0.1 × (1 - \frac{delinqcount + latepaycount + choffs + bankr}{10}), \text{where each component is capped at 1.0}",calculation_knowledge,-1
745,credit,6,Financial Stability Index (FSI),"Measures a customer's overall financial stability combining income, savings, and debt factors.","FSI = 0.3 × (1 - debincratio) + 0.3 × \frac{liqassets}{mthincome × 6} + 0.2 × \frac{bankaccbal}{mthincome × 3} + 0.2 × \frac{savamount}{mthincome × 12}, \text{where each component is capped at 1.0}",calculation_knowledge,[0]
746,credit,7,Customer Engagement Score (CES),Quantifies how actively a customer uses bank products and services.,"CES = 0.4 × produsescore + 0.3 × chanusescore + 0.2 × bankrelscore + 0.1 × \frac{custservint}{10}, \text{where each component is capped at 1.0}",calculation_knowledge,-1
747,credit,8,Risk-Adjusted Return (RAR),Measures the profitability of a customer relationship adjusted for credit risk.,"RAR = profitscore × (1 - \frac{risklev}{4}), \text{where risklev is converted to a numeric scale: Low=1, Medium=2, High=3, Very High=4}",calculation_knowledge,-1
748,credit,9,Account Health Index (AHI),"Composite measure of account quality considering age, mix, and payment history.","AHI = 0.4 × \frac{avgaccage}{10} + 0.3 × accmixscore + 0.3 × payconsist, \text{where each component is capped at 1.0}",calculation_knowledge,-1
749,credit,10,Prime Customer,Identifies customers with excellent creditworthiness and financial stability.,"A customer with credscore > 720, defhist of 'Excellent' or 'Good', and risklev of 'Low'.",domain_knowledge,-1
750,credit,11,Financially Vulnerable,Identifies customers who may be financially stressed or at risk.,"A customer with debincratio > 0.5, liqassets < mthincome × 3, and at least one of: delinqcount > 0, latepaycount > 1, or ovrfreq of 'Frequent'.",domain_knowledge,[0]
751,credit,12,High-Value Customer,Identifies customers with significant value to the institution.,"A customer with custlifeval in the top quartile, tenureyrs > 5, and crossratio > 0.5.",domain_knowledge,-1
752,credit,13,Credit Builder,Identifies customers actively working to establish or improve credit.,"A customer with credageyrs < 3, credinq > 2 in the past year, and recentbeh of 'Improving'.",domain_knowledge,-1
753,credit,14,Digital First Customer,Identifies customers who primarily engage through digital channels.,"A customer with chaninvdatablock.onlineuse of 'High' or chaninvdatablock.mobileuse of 'High', and chaninvdatablock.autopay of 'Yes'.",domain_knowledge,-1
754,credit,15,Investment Focused,Identifies customers with significant investment activity and sophistication.,"A customer with chaninvdatablock.invcluster.investport of 'Moderate' or 'Aggressive', chaninvdatablock.invcluster.investexp of 'Extensive', and investamt > 0.3 × totassets.",domain_knowledge,-1
755,credit,16,Revolving Credit Dependent,Identifies customers who heavily rely on revolving credit.,"A customer with credutil > 0.7, cardcount > 2, and cardpayhist of 'Fair' or 'Poor'.",domain_knowledge,[1]
756,credit,17,Property Risk Exposure,Assesses risk related to a customer's property investment.,"A customer with propfinancialdata.propown of 'Own', LTV > 0.8, and propfinancialdata.mortgagebits.mortpayhist of 'Fair' or 'Poor'.",domain_knowledge,[2]
757,credit,18,Frequent Credit Seeker,Identifies customers frequently seeking new credit.,"A customer with hardinq > 3 in the past six months, seekbeh of 'High', and newaccage < 1.",domain_knowledge,-1
758,credit,19,Over-Extended,Identifies customers who are financially over-extended.,"A customer with DTI > 0.43, CUR > 0.8, and at least one of: ovrfreq of 'Frequent', bouncecount > 0 in the past three months.",domain_knowledge,"[0, 1]"
759,credit,20,Credit Score Categories,Illustrates the meaning of different credit score ranges.,"Credit scores (credscore) typically range from 300-850. 300-579: Poor, 580-669: Fair, 670-739: Good, 740-799: Very Good, 800-850: Excellent, Otherwise: Unknown. Higher scores indicate lower credit risk.",value_illustration,-1
760,credit,21,Income Stability Score,Illustrates the meaning of income stability scores.,"Income stability (incstabscore) ranges from 0-10. Scores below 3 indicate highly variable income, 3-6 indicates moderate stability, and above 6 indicates highly stable income sources.",value_illustration,-1
761,credit,22,Debt-to-Income Ratio Interpretation,Illustrates what different debt-to-income ratios mean for lending decisions.,"Debt-to-Income ratio (debincratio) ranges from 0-1 (or above). Below 0.36 is typically considered excellent, 0.36-0.43 is good, 0.43-0.50 is concerning, and above 0.50 is risky for new credit approval.",value_illustration,[0]
762,credit,23,Credit Utilization Impact,Illustrates how credit utilization affects credit scores.,"Credit Utilization (credutil) ranges from 0-1 (or above). Utilization under 0.30 is optimal for credit scores, 0.30-0.50 has moderate negative impact, 0.50-0.70 has significant negative impact, and above 0.70 severely impacts credit scores.",value_illustration,[1]
763,credit,24,Loan-to-Value Ratio Significance,Illustrates what different LTV ratios mean for mortgage lending.,"Loan-to-Value ratio (LTV) typically ranges from 0-1 (or above). Below 0.80 generally avoids private mortgage insurance requirements, 0.80-0.95 typically requires PMI, and above 0.95 indicates high leverage and increased lending risk.",value_illustration,[2]
764,credit,25,Risk Level Classifications,Illustrates what different risk level classifications mean.,"Risk level (risklev) values indicate likelihood of default or financial difficulty. 'Low' indicates minimal risk, 'Medium' indicates moderate risk requiring standard monitoring, 'High' indicates significant risk requiring enhanced monitoring, and 'Very High' indicates severe risk requiring active intervention.",value_illustration,-1
765,credit,26,Payment History Quality,Illustrates what different payment history classifications indicate.,"Payment history (defhist, mortpayhist, rentpayhist, cardpayhist, loanpayhist) classifications indicate reliability. 'Excellent' indicates no late payments, 'Good' indicates minimal late payments, 'Fair' indicates occasional missed payments, 'Poor' indicates regular missed payments, 'Current' indicates being up to date, and 'Past' indicates historical data.",value_illustration,-1
766,credit,27,Cross-Sell Ratio Meaning,Illustrates what different cross-sell ratio values indicate.,"Cross-sell ratio (crossratio) ranges from 0-1. Values below 0.2 indicate minimal product relationships, 0.2-0.5 indicates moderate opportunity, and above 0.5 indicates strong existing relationship with high additional sales potential.",value_illustration,-1
767,credit,28,Account Mix Score Interpretation,Illustrates what different account mix scores represent.,"Account mix score (accmixscore) ranges from 0-1. Higher scores indicate a healthy diversity of account types (revolving, installment, mortgage, etc.), which positively impacts credit scores and indicates financial sophistication.",value_illustration,-1
768,credit,29,Churn Rate Significance,Illustrates what different churn rate values indicate about customer retention risk.,"Churn rate (churnrate) ranges from 0-1. Values below 0.1 indicate low attrition risk, 0.1-0.2 indicates moderate risk, 0.2-0.3 indicates high risk, and above 0.3 indicates severe risk of customer loss requiring immediate relationship management intervention.",value_illustration,-1
769,credit,30,Total Debt Service Ratio (TDSR),Extended debt ratio that accounts for all financial obligations including housing costs.,"TDSR = DTI + \frac{\text{Housing Costs}}{\text{Monthly Income}}, \text{where Housing Costs are determined by propfinancialdata and DTI is the Debt-to-Income Ratio}",calculation_knowledge,[0]
770,credit,31,Credit Quality Index (CQI),Comprehensive measure of overall credit quality incorporating credit score and utilization.,"CQI = 0.6 × \frac{credscore}{850} + 0.4 × (1 - CUR), \text{where CUR is the Credit Utilization Ratio}",calculation_knowledge,[1]
771,credit,32,Housing Affordability Ratio (HAR),Measures the affordability of housing costs relative to income.,"HAR = \frac{\text{Monthly Housing Payment}}{\text{Monthly Income}} × 100\%, \text{where Monthly Housing Payment is derived from propfinancialdata and LTV calculations}",calculation_knowledge,[2]
772,credit,33,Financial Vulnerability Score (FVS),Quantifies financial fragility by combining debt burden and savings adequacy.,"FVS = 0.5 × DTI + 0.5 × (1 - \frac{liqassets}{mthincome × 6}), \text{where DTI is the Debt-to-Income Ratio and the second term measures emergency fund adequacy}",calculation_knowledge,[0]
773,credit,34,Customer Retention Risk (CRR),Calculates risk of customer attrition based on engagement and satisfaction metrics.,"CRR = 0.4 × churnrate + 0.3 × (1 - CES) + 0.3 × \frac{complainthist}{3}, \text{where CES is the Customer Engagement Score and complainthist is converted to numeric (Low=1, Medium=2, High=3)}",calculation_knowledge,[7]
774,credit,35,Asset Liquidity Ratio (ALR),Measures the proportion of customer assets that can be quickly converted to cash.,"ALR = \frac{liqassets}{totassets} = \frac{liqassets}{Net Worth + totliabs}, \text{where Net Worth is the difference between assets and liabilities}",calculation_knowledge,[4]
775,credit,36,Credit Risk Intensity (CRI),Advanced measure of credit risk that incorporates payment history and account diversity.,"CRI = 0.5 × (1 - \frac{credscore}{850}) + 0.3 × \frac{delinqcount + latepaycount + choffs}{10} + 0.2 × (1 - AHI), \text{where AHI is the Account Health Index}",calculation_knowledge,[9]
776,credit,37,Investment Portfolio Quality (IPQ),Evaluates the quality and performance of customer's investment allocations.,"IPQ = 0.4 × RAR + 0.4 × \frac{investamt}{totassets} + 0.2 × \frac{chaninvdatablock.invcluster.investexp}{3}, \text{where RAR is the Risk-Adjusted Return and investexp is converted to numeric (Limited=1, Moderate=2, Extensive=3)}",calculation_knowledge,[8]
777,credit,38,Banking Relationship Strength (BRS),Quantifies the depth and quality of a customer's banking relationship.,"BRS = 0.3 × bankrelscore + 0.3 × (1 - churnrate) + 0.4 × CES, \text{where CES is the Customer Engagement Score}",calculation_knowledge,[7]
778,credit,39,Credit Health Momentum (CHM),Measures the trajectory of a customer's credit health over time.,"CHM = CHS × (1 + \Delta_\text{recentbeh}), \text{where CHS is the Credit Health Score and } \Delta_\text{recentbeh} \text{ is +0.1 for 'Improving', 0 for 'Stable', and -0.1 for 'Deteriorating'}",calculation_knowledge,[5]
779,credit,40,Mortgage Risk Profile,Identifies customers with elevated mortgage-related risk factors.,"A customer with high LTV (Loan-to-Value Ratio > 0.9), negative equity risk (LTV > 1.0), or payment stress (propfinancialdata.mortgagebits.mortpayhist of 'Fair' or 'Poor').",domain_knowledge,[2]
780,credit,41,Credit Utilization Alert,Identifies customers with problematic credit utilization patterns.,"A customer with CUR (Credit Utilization Ratio) > 0.8, an increasing trend in utilization, and limited available credit (totcredlimit < mthincome × 2).",domain_knowledge,[1]
781,credit,42,Financial Stress Indicator,Identifies customers showing multiple signs of financial difficulty.,"A customer with FVS (Financial Vulnerability Score) > 0.7, recent payment issues (delinqcount > 0 or latepaycount > 0 in past six months), and negative Net Worth.",domain_knowledge,"[4, 33]"
782,credit,43,Premium Banking Candidate,Identifies customers who are good candidates for premium banking services.,"A customer with high CQI (Credit Quality Index > 0.8), strong FSI (Financial Stability Index > 0.7), and significant assets (totassets > $250,000).",domain_knowledge,"[6, 31]"
783,credit,44,Digital Channel Opportunity,Identifies customers who would benefit from increased digital engagement.,A customer with low digital engagement (chaninvdatablock.onlineuse not 'High' and chaninvdatablock.mobileuse not 'High') but high BRS (Banking Relationship Strength > 0.7) and multiple products (produsescore > 0.5).,domain_knowledge,[38]
784,credit,45,Credit Building Opportunity,Identifies customers who would benefit from credit-building products.,"A customer with limited credit history (credageyrs < 2), low CQI (Credit Quality Index < 0.6), but positive banking behavior (bouncecount = 0 and bankrelscore > 0.6).",domain_knowledge,[31]
785,credit,46,Investment Services Target,Identifies customers who are good candidates for investment services.,"A customer with high ALR (Asset Liquidity Ratio > 0.3) and strong income (mthincome > $5,000).",domain_knowledge,[35]
786,credit,47,Declining Credit Health,Identifies customers with deteriorating credit health requiring intervention.,"A customer with negative CHM (Credit Health Momentum < 0), increasing CRI (Credit Risk Intensity growing by >10% in 6 months), and rising DTI (Debt-to-Income Ratio increasing by >5% in 6 months).",domain_knowledge,"[0, 36, 39]"
787,credit,48,Relationship Attrition Risk,Identifies customers at high risk of ending their banking relationship.,"A customer with high CRR (Customer Retention Risk > 0.7), declining product usage (decreasing produsescore), and competitive shopping behavior (hardinq > 2 in past 3 months).",domain_knowledge,[34]
788,credit,49,Cross-Sell Priority,Identifies customers who should be prioritized for cross-selling efforts.,"A customer with strong CES (Customer Engagement Score > 0.7), positive CQI (Credit Quality Index > 0.7), and unrealized product potential (crossratio > 0.5 but produsescore < 0.5).",domain_knowledge,"[7, 31]"
789,credit,50,High Engagement Criteria,Defines customers with a high level of engagement with bank products and services.,A Customer Engagement Score (CES) greater than 0.7.,domain_knowledge,[7]
790,credit,51,Cohort Quarter,Quarter of the year when the customer started with the institution.,Quarter of the year when the customer started with the institution (scoring date minus tenure years),domain_knowledge,-1
791,cross_db,0,Data Transfer Efficiency (DTE),Measures the efficiency of data transfers based on success rate and error count.,DTE = \frac{\text{SuccessPct}}{\text{ErrTally} + 1},calculation_knowledge,-1
792,cross_db,1,Bandwidth Saturation Index (BSI),Quantifies how close a data flow is to saturating available bandwidth.,BSI = \text{BwidthPct} \times \frac{\text{DataSizeMB}}{\text{DurMin}},calculation_knowledge,-1
793,cross_db,2,Risk Exposure Score (RES),Calculates the overall risk exposure by combining risk assessment and residual risk.,RES = \text{RiskAssess} \times \text{CtrlEff}^{-1},calculation_knowledge,-1
794,cross_db,3,Compliance Cost Ratio (CCR),Evaluates the cost of compliance relative to potential penalties.,CCR = \frac{\text{CostUSD}}{\text{PenUSD} + 1},calculation_knowledge,-1
795,cross_db,4,Data Sensitivity Index (DSI),Quantifies the sensitivity of data based on volume and sensitivity level.,DSI = \text{VolGB} \times \begin{cases} 3 & \text{if DataSense = 'High'} \\ 2 & \text{if DataSense = 'Medium'} \\ 1 & \text{if DataSense = 'Low'} \end{cases},calculation_knowledge,-1
796,cross_db,5,Security Robustness Score (SRS),Measures the strength of security controls based on encryption and access controls.,SRS = \begin{cases} 3 & \text{if EncState = 'Full' and AclState = 'Strong'} \\ 2 & \text{if EncState = 'Full' or AclState = 'Adequate'} \\ 1 & \text{otherwise} \end{cases},calculation_knowledge,-1
797,cross_db,6,Vendor Reliability Index (VRI),Assesses vendor reliability based on security rating and contract status.,VRI = \text{VendSecRate value} \times \begin{cases} 1 & \text{if ContrState = 'Active'} \\ 0.5 & \text{otherwise} \end{cases},calculation_knowledge,-1
798,cross_db,7,Audit Finding Severity (AFS),Quantifies the severity of audit findings based on critical findings.,AFS = \frac{\text{CritFindNum}}{\text{FindTally} + 1},calculation_knowledge,-1
799,cross_db,8,Data Subject Request Load (DSRL),Measures the workload from data subject requests.,DSRL = \text{AccReqNum} + \text{DelReqNum} + \text{RectReqNum} + \text{PortReqNum},calculation_knowledge,-1
800,cross_db,9,Cross-Border Risk Factor (CBRF),Evaluates risk associated with cross-border data transfers.,CBRF = \text{RES} \times \begin{cases} 2 & \text{if OrigNation \neq DestNation} \\ 1 & \text{otherwise} \end{cases},calculation_knowledge,[2]
801,cross_db,10,High-Risk Data Flow,Identifies data flows with elevated risk based on risk exposure and sensitivity.,A data flow where RES > 0.7 and DSI > 100,domain_knowledge,"[2, 4]"
802,cross_db,11,Secure Data Flow,Classifies data flows with strong security controls.,A data flow where SRS = 3,domain_knowledge,[5]
803,cross_db,12,Non-Compliant Vendor,Identifies vendors failing compliance standards.,A vendor where PolComp = 'Non-compliant' or ProcComp = 'Non-compliant',domain_knowledge,-1
804,cross_db,13,Critical Audit Issue,Flags audits with significant issues requiring urgent remediation.,An audit where AFS > 0.5,domain_knowledge,[7]
805,cross_db,14,Sensitive Data Exposure,Highlights data profiles with high sensitivity and weak security.,A data profile where DSI > 100 and SRS < 2,domain_knowledge,"[4, 5]"
806,cross_db,15,Cross-Border Compliance Gap,Identifies compliance issues in cross-border data flows.,A compliance record where GdprComp = 'Non-compliant' or LocLawComp = 'Non-compliant' and OrigNation ≠ DestNation,domain_knowledge,-1
807,cross_db,16,Vendor Risk Tier,Categorizes vendors into risk tiers based on security and compliance.,"A vendor is: High Risk if VRI < 2, Medium Risk if 2 ≤ VRI < 3, Low Risk if VRI ≥ 3",domain_knowledge,[6]
808,cross_db,17,Data Integrity Failure,Identifies data profiles with failed integrity checks.,A data profile where IntCheck = 'Failed' or CsumVerify = 'Failed',domain_knowledge,-1
809,cross_db,18,Overloaded Data Flow,Flags data flows with high bandwidth saturation and low efficiency.,A data flow where BSI > 50 and DTE < 1.0,domain_knowledge,"[0, 1]"
810,cross_db,19,Regulatory Risk Exposure,Identifies data flows with high regulatory risk due to compliance gaps and cross-border transfers.,A data flow with CBRF > 1.5 and Cross-Border Compliance Gap exists,domain_knowledge,"[9, 15]"
811,cross_db,20,DataFlow.SuccessPct,Illustrates the success rate of data transfers.,"Ranges from 0 to 100%. A SuccessPct of 95% indicates reliable transfers, while <80% suggests frequent failures, from DataFlow.",value_illustration,-1
812,cross_db,21,RiskManagement.RiskAssess,Illustrates the risk assessment score.,"Ranges from 0 to 100. A RiskAssess > 80 indicates high risk, while <20 suggests minimal risk, from RiskManagement.",value_illustration,-1
813,cross_db,22,DataProfile.VolGB,Illustrates the volume of data in gigabytes.,"Ranges from 0 to millions. A VolGB of 1000 might represent a large dataset, while 0.1 is typical for small logs, from DataProfile.",value_illustration,-1
814,cross_db,23,SecurityProfile.EncState,Illustrates the encryption status of data.,"Enum: 'Full', 'Partial'. 'Full' indicates all data is encrypted, while 'Partial' suggests gaps, from SecurityProfile.",value_illustration,-1
815,cross_db,24,VendorManagement.VendSecRate,Illustrates the vendor security rating.,"Enum: 'A' = 4, 'B' = 3, 'C' = 2, 'D' or others = 1. This numeric scale quantifies vendor security, where 'A' reflects top-tier security (score 4), and lower ratings (down to 'D') indicate progressively weaker security controls.",value_illustration,-1
816,cross_db,25,Compliance.GdprComp,Illustrates GDPR compliance status.,"Enum: 'Compliant', 'Non-compliant', 'Partial'. 'Compliant' meets all GDPR rules, 'Non-compliant' fails key requirements, from Compliance.",value_illustration,-1
817,cross_db,26,AuditAndCompliance.RespTimeDay,Illustrates the response time for data subject requests.,"Ranges from 0 to days. A RespTimeDay of 1.5 suggests quick responses, while >7 indicates delays, from AuditAndCompliance.",value_illustration,-1
818,cross_db,27,DataFlow.ErrTally,Illustrates the count of errors in data transfers.,"Integer ≥ 0. An ErrTally of 0 indicates flawless transfers, while >10 suggests reliability issues, from DataFlow.",value_illustration,-1
819,cross_db,28,RiskManagement.CtrlEff,Illustrates the effectiveness of risk controls.,"Ranges from 0 to 100. A CtrlEff of 90% shows strong controls, while <50% indicates weaknesses, from RiskManagement.",value_illustration,-1
820,cross_db,29,SecurityProfile.LogRetDays,Illustrates the retention period for audit logs.,"Integer ≥ 0 days. A LogRetDays of 365 meets many compliance needs, while <30 may violate regulations, from SecurityProfile.",value_illustration,-1
821,cross_db,30,Data Flow Reliability Score (DFRS),Quantifies the reliability of a data flow based on success rate and retry attempts.,DFRS = \text{DTE} \times (1 - \text{RtryTally} / (\text{ErrTally} + 1)),calculation_knowledge,"[0, 27]"
822,cross_db,31,Security Control Cost Ratio (SCCR),Evaluates the cost-effectiveness of security controls relative to compliance costs.,SCCR = \text{SRS} / (\text{CostUSD} + 1),calculation_knowledge,[5]
823,cross_db,32,Vendor Compliance Burden (VCB),Measures the compliance burden of a vendor based on audit findings and security rating.,VCB = \text{AFS} \times (5 - \text{VendSecRate value}),calculation_knowledge,"[7, 24]"
824,cross_db,33,Cross-Border Data Volume Risk (CDVR),Assesses risk from large cross-border data volumes.,CDVR = \text{CBRF} \times \text{VolGB},calculation_knowledge,"[9, 22]"
825,cross_db,34,Data Subject Request Pressure (DSRP),Quantifies pressure from data subject requests relative to response time.,DSRP = \text{DSRL} \times \text{RespTimeDay},calculation_knowledge,"[8, 26]"
826,cross_db,35,Encryption Coverage Ratio (ECR),Measures the extent of encryption coverage relative to data sensitivity.,ECR = \text{SRS} \times \text{DSI},calculation_knowledge,"[4, 5]"
827,cross_db,36,Audit Remediation Load (ARL),Calculates the workload required for audit remediation based on findings and compliance gaps.,ARL = \text{AFS} \times \text{DSRL},calculation_knowledge,"[7, 8, 25]"
828,cross_db,37,Bandwidth Risk Factor (BRF),Evaluates risk from bandwidth overuse in sensitive data flows.,BRF = \text{BSI} \times \text{DSI},calculation_knowledge,"[1, 4]"
829,cross_db,38,Vendor Risk Amplification (VRA),Quantifies how vendor issues amplify overall risk exposure.,VRA = \text{VRI} \times \text{RES},calculation_knowledge,"[2, 6]"
830,cross_db,39,Critical Data Flow Risk,Identifies data flows with both high risk exposure and poor reliability.,A data flow where RES > 0.7 and DFRS < 0.5,domain_knowledge,"[2, 30]"
831,cross_db,40,Overburdened Compliance Flow,Flags data flows with high compliance costs and audit remediation needs.,A data flow where CCR > 0.8 and ARL > 10,domain_knowledge,"[3, 36]"
832,cross_db,41,Unprotected Sensitive Data,Identifies sensitive data lacking adequate encryption coverage.,A data profile where DSI > 100 and ECR < 2,domain_knowledge,"[4, 35]"
833,cross_db,42,High-Pressure Data Flow,Highlights data flows under strain from data subject requests and bandwidth saturation.,A data flow where DSRP > 50 and BSI > 50,domain_knowledge,"[1, 34]"
834,cross_db,43,Vendor-Driven Risk Flow,Identifies data flows with elevated risk due to vendor issues.,A data flow where VRA > 3 and VCB > 2,domain_knowledge,"[32, 38]"
835,cross_db,44,Cross-Border Audit Risk,Flags cross-border data flows with significant audit issues.,A data flow where CDVR > 1000 and AFS > 0.5,domain_knowledge,"[7, 33]"
836,cross_db,45,Insecure High-Volume Flow,Identifies high-volume data flows with weak security controls.,A data flow where VolGB > 500 and SRS < 2,domain_knowledge,"[5, 22]"
837,cross_db,46,Regulatory Overload Flow,Highlights data flows with both regulatory risk and compliance gaps.,A data flow with Regulatory Risk Exposure and GdprComp = 'Non-compliant',domain_knowledge,"[19, 25]"
838,cross_db,47,Bandwidth-Constrained Risk,Identifies data flows where bandwidth saturation amplifies risk.,A data flow where BRF > 100 and RES > 0.7,domain_knowledge,"[2, 37]"
839,cross_db,48,Incident Resolution Efficiency (IRE),Measures how efficiently incidents are resolved relative to SLA compliance.,"IRE = \text{SLApct} / (\text{AvgResolHrs} + 1), \text{where SLApct and AvgResolHrs are from RiskManagement, adding 1 to avoid division by zero.}",calculation_knowledge,-1
840,cross_db,49,Incident-Prone Data Flow,Flags data flows with poor incident resolution and high risk.,A data flow where IRE < 0.5 and High-Risk Data Flow,domain_knowledge,"[10, 30]"
841,cross_db,50,Data Flow Stability Index (DFSI),Quantifies the stability of data flows by balancing reliability and error recovery.,DFSI = \text{DFRS} \times \frac{\text{SuccessPct}}{\text{ErrTally} + 1},calculation_knowledge,"[20, 27, 30]"
842,cross_db,51,Compliance Overhead Ratio (COR),Measures the operational burden of compliance relative to data subject request load.,COR = \text{DSRP} / (\text{CostUSD} + 1),calculation_knowledge,[34]
843,cross_db,52,Security Posture Maturity (SPM),Evaluates the maturity of security controls based on encryption and audit log retention.,SPM = \text{ECR} \times \frac{\text{LogRetDays}}{365},calculation_knowledge,"[29, 35]"
844,cross_db,53,Vendor Risk Concentration (VRC),Assesses the concentration of risk from a vendor’s compliance and security issues.,VRC = \text{VRA} \times (1 - \text{VRI}),calculation_knowledge,"[6, 38]"
845,cross_db,54,Cross-Border Compliance Exposure (CBCE),Quantifies compliance risk for cross-border flows based on regulatory gaps and volume.,CBCE = \text{CDVR} \times \begin{cases} 2 & \text{if GdprComp = 'Non-compliant'} \\ 1 & \text{otherwise} \end{cases},calculation_knowledge,"[25, 33]"
846,cross_db,55,Incident Impact Factor (IIF),Measures the potential impact of incidents based on risk exposure and resolution efficiency.,IIF = \text{RES} \times (1 - \text{IRE}),calculation_knowledge,"[2, 48]"
847,cross_db,56,Data Retention Risk Score (DRRS),Evaluates risk from prolonged data retention relative to sensitivity.,DRRS = \text{DSI} \times \frac{\text{RetDays}}{365},calculation_knowledge,[4]
848,cross_db,57,Audit Compliance Pressure (ACP),Quantifies pressure from audit findings and compliance remediation needs.,ACP = \text{ARL} \times \text{AFS},calculation_knowledge,"[7, 36]"
849,cross_db,58,Bandwidth Compliance Risk (BCR),Assesses compliance risk from bandwidth-constrained data flows.,BCR = \text{BRF} \times \begin{cases} 1.5 & \text{if GdprComp = 'Partial'} \\ 2 & \text{if GdprComp = 'Non-compliant'} \\ 1 & \text{otherwise} \end{cases},calculation_knowledge,"[25, 37]"
850,cross_db,59,Vendor Security Cost Index (VSCI),Evaluates the cost-effectiveness of vendor security relative to compliance burden.,VSCI = \text{VCB} / (\text{SCCR} + 1),calculation_knowledge,"[31, 32]"
851,cross_db,60,Unstable High-Risk Flow,Identifies high-risk data flows with poor stability.,A data flow where DFSI < 0.5 and Critical Data Flow Risk exists,domain_knowledge,"[39, 50]"
852,cross_db,61,Overloaded Security Flow,Flags data flows with high security burden and compliance exposure.,A data flow where SPM < 1 and CBCE > 100,domain_knowledge,"[52, 54]"
853,cross_db,62,Excessive Retention Risk,Highlights data profiles with prolonged retention and high sensitivity.,A data profile where DRRS > 50 and DSI > 100,domain_knowledge,"[4, 56]"
854,cross_db,63,Vendor Compliance Risk Cluster,Identifies vendors contributing to concentrated compliance risks.,A vendor where VRC > 2 and VCB > 2,domain_knowledge,"[32, 53]"
855,cross_db,64,Incident-Prone Compliance Flow,Flags data flows with high incident impact and compliance gaps.,A data flow where IIF > 0.8 and GdprComp = 'Non-compliant',domain_knowledge,"[25, 55]"
856,cross_db,65,Audit-Stressed Data Flow,Identifies data flows under pressure from audit findings and compliance burdens.,A data flow where ACP > 5 and COR > 0.5.,domain_knowledge,"[51, 57]"
857,cross_db,66,Bandwidth-Limited Compliance Risk,Highlights data flows where bandwidth constraints exacerbate compliance risks.,A data flow where BCR > 50 and Cross-Border Compliance Gap exists,domain_knowledge,"[15, 58]"
858,cross_db,67,Costly Vendor Risk Flow,Identifies data flows with high vendor-related costs and risks.,A data flow where VSCI > 1 and VRA > 3,domain_knowledge,"[38, 59]"
859,cross_db,68,Sensitive Unstable Flow,Flags sensitive data flows with stability issues.,A data flow where DFSI < 0.5 and Sensitive Data Exposure exists,domain_knowledge,"[14, 50]"
860,cross_db,69,High-Impact Audit Risk Flow,Identifies data flows with severe audit findings and regulatory risks.,A data flow where Regulatory Risk Exposure exists and ACP > 5,domain_knowledge,"[19, 57]"
861,cross_db,70,Transfer Path,Describes the data flow path from origin to destination nation.,A string concatenating OrigNation and DestNation as 'OrigNation -> DestNation',domain_knowledge,-1
862,cross_db,71,Request Breakdown,Describes the types and counts of data subject requests.,"An array of strings listing request types and their counts: 'Access: AccReqNum', 'Deletion: DelReqNum', 'Rectification: RectReqNum', 'Portability: PortReqNum', unnested for display",domain_knowledge,-1
863,cross_db,72,Integrity Failure Count (IFC),Counts the number of failed integrity checks per data profile.,IFC = \begin{cases} 1 & \text{if IntCheck = 'Failed'} \\ 0 & \text{otherwise} \end{cases} + \begin{cases} 1 & \text{if CsumVerify = 'Failed'} \\ 0 & \text{otherwise} \end{cases},calculation_knowledge,-1
864,cross_db,73,Failure Types List,Concatenates the types of integrity failures for a data profile into a single string.,"A comma-separated string listing failure types: 'Integrity Check' if IntCheck = 'Failed', 'Checksum Verification' if CsumVerify = 'Failed'.",domain_knowledge,-1
865,cross_db,74,High Audit Compliance Pressure,Identifies data flows with elevated audit compliance pressure based on audit findings and data subject request load.,A data flow where ACP > 5,domain_knowledge,[57]
866,cross_db,75,Cross-Border Data Flow,Identifies data flows where the origin and destination nations differ.,A data flow where OrigNation != DestNation,domain_knowledge,-1
867,cross_db,76,Slow Remediation Timeline,Identifies data flows where the remediation deadline has passed.,A data flow where CURRENT_DATE - RemedDue > 0,domain_knowledge,-1
868,cross_db,77,Nearing Remediation Deadline,Identifies data flows where the remediation deadline is within 5 days.,A data flow where (CURRENT_DATE - RemedDue) is between -5 and 0,domain_knowledge,-1
869,cross_db,78,High Vendor Risk Concentration,Identifies vendors with elevated risk concentration based on vendor risk amplification and reliability.,A data flow where CURRENT_DATE - RemedDue > 0,domain_knowledge,-1
870,virtual,0,fans.statustag,Explains the meaning of fan account status and its impact on platform interactions,"Fan status typically falls into four categories: 'Active' indicates normal users with full access to all features; 'VIP' represents premium users with privileged services; 'Inactive' refers to dormant accounts with no login for over 30 days, with some features restricted; 'Blocked' means accounts temporarily or permanently banned due to violations, with no access to platform features.",value_illustration,-1
871,virtual,1,fans.tierstep,Explains the practical significance of fan tier levels,"Fan tiers (tierstep) start at 1 and increase progressively, representing different loyalty levels and privileges: Tiers 1-3 are 'Entry-level' fans (newly joined), tiers 4-7 represent 'Mid-level' fans (stable supporters), tiers 8-10 are 'High-level fans (long-term loyal followers), and tiers above 10 are 'Core' fans (foundational idol supporters). Any accounts with missing or invalid tier values are classified as 'Undefined' and require administrative review. Each tier level grants new privileges and increases platform visibility.",value_illustration,-1
872,virtual,2,virtualidols.kindtag,Explains technical and performance characteristics of different virtual idol types,"'2D' represents two-dimensional animated characters, suitable for anime-style performances with limited interactivity; '3D' refers to three-dimensional modeled characters with more natural movements capable of complex performances; 'AI Generated' indicates characters created using AI technology with high uniqueness but requiring consistency monitoring; 'Mixed Reality' describes characters that can interact with real-world elements, requiring the highest technical threshold.",value_illustration,-1
873,virtual,3,membershipandspending.membkind,Explains the benefit differences between membership types,"'Free' users can access only basic content with approximately 10 hours of viewable content per month; 'Basic' members (about $5 monthly) receive reduced ads and access to additional content; 'Premium' members (about $10-15 monthly) get ad-free experience, exclusive content, and priority viewing; 'Diamond' members (above $25 monthly) receive all benefits plus 1-on-1 interaction opportunities and physical merchandise.",value_illustration,-1
874,virtual,4,engagement.actfreq,Analyzes the significance of interaction frequency for fan classification,"'Daily' users log into the platform at least once per day on average; 'Weekly' users log in 3-5 times per week; 'Monthly' users log in 1-2 times per month; 'Occasional' users have login intervals exceeding one month. Interaction frequency affects platform push priority, content recommendations, and event invitations, with Daily users considered the platform's core active group.",value_illustration,-1
875,virtual,5,engagement.engrate,Explains the calculation basis and business significance of fan engagement rate,"Engagement rate (engrate) typically ranges from 0 to 1.000, with higher values indicating more active fans. 0-0.200 represents low engagement (viewing only without participation), 0.201-0.500 indicates moderate engagement (occasional comments and likes), 0.501-0.800 shows high engagement (frequent comments and sharing), and 0.801-1.000 represents ultra-high engagement (comprehensive deep participation in content co-creation). Platforms generally consider rates above 0.500 as quality fans.",value_illustration,-1
876,virtual,6,socialcommunity.community_engagement.content_creation.contqualrate,Explains content quality rating standards and classification,"Content quality rating (contqualrate) ranges from 0-10.0: 0-3.9 points indicate low-quality content (potentially violating rules or poor quality); 4.0-6.9 points represent medium-quality content (meeting basic platform standards); 7.0-8.9 points indicate high-quality content (well-produced and creative); 9.0-10.0 points represent premium content (platform recommendation level). Quality ratings affect content recommendations, exposure, and creator incentives.",value_illustration,-1
877,virtual,7,interactions.gifttot_interactions.giftvalusd,Explains the statistical significance of gift quantity and value,"gifttot represents the total number of gifts sent by fans, typically classified as: fewer than 10 is considered minimal gifting, 10-50 represents moderate support, and above 50 indicates substantial support; giftvalusd represents the USD value of gifts, generally $1-5 for small support, $5-20 for moderate support, $20-100 for large support, and above $100 for super support. Both metrics combined assess a fan's economic support level.",value_illustration,-1
878,virtual,8,retentionandinfluence.churnflag,Explains the judgment basis for churn risk rating,"'None' indicates extremely low churn risk, with users maintaining high-frequency interaction and recent consumption; 'Low' represents slight churn risk with slightly decreased login frequency; 'Medium' indicates moderate churn risk with significantly decreased interaction and consumption frequency and increased login intervals; 'High' represents severe churn risk with no logins or interactions in the past 14-30 days, requiring immediate intervention.",value_illustration,-1
879,virtual,9,loyaltyandachievements.reward_progress.loyalty.loypts,Explains the accumulation and application value of loyalty points,"Loyalty points (loypts) are cumulative rewards for fan activities: 0-1000 points is entry-level (redeemable for basic digital items); 1001-5000 points is intermediate level (redeemable for limited-time privileges and mid-level digital items); 5001-20000 points is advanced level (redeemable for limited merchandise and activity priorities); above 20000 points is expert level (invitations to participate in platform decisions and idol development). Points are typically earned through logins, purchases, and activity participation.",value_illustration,-1
880,virtual,10,Fan Engagement Index (FEI),A comprehensive metric measuring fan activity level with platform and virtual idols,"FEI = (engrate \times 0.4) + (\frac{socintscore}{100} \times 0.3) + (\frac{actdayswk}{7} \times 0.2) + (\frac{avgsesscount}{10} \times 0.1), \text{ where weights reflect each factor's different contribution to engagement, with results ranging from 0-1. Higher values indicate more engaged fans.}",calculation_knowledge,-1
881,virtual,11,Monetization Value (MV),Calculates the monetary value a fan brings to the platform over time,"MV = spendusd \times \left(1 + \frac{membdays}{365} \times 0.5\right) \times \left(1 + \frac{gifttot}{10} \times 0.2\right), \text{ where the formula adjusts spending based on membership duration and gift activity to reflect long-term value.}",calculation_knowledge,-1
882,virtual,12,Content Creation Impact Score (CCIS),Measures the impact of fan-created content on the community,"CCIS = contqualrate \times \left(\frac{ugcval}{10}\right) \times \left(1 + \frac{follcount}{100} \times 0.5\right), \text{ where content quality is multiplied by normalized content volume and amplified by follower reach.}",calculation_knowledge,-1
883,virtual,13,Retention Risk Factor (RRF),Quantifies the risk of fan churn based on multiple behavioral indicators,"RRF = (1 - intconsist) \times 2 + \left(\frac{CURRENT\_DATE - lastlogdt}{30}\right) \times 0.5 + \left(\frac{churnflag\_numeric}{3}\right) \times 2, \text{ where churnflag\_numeric maps churnflag enum: None=0, Low=1, Medium=2, High=3.}",calculation_knowledge,[52]
884,virtual,14,Social Influence Multiplier (SIM),Calculates how much a fan amplifies content through their social network,"SIM = \left(\frac{follcount}{100}\right) \times (engrate \times 2) \times (viralcont + 1) \times 0.5, \text{ reflecting the fan's ability to spread content through their network based on their following, engagement level, and history of creating viral content.}",calculation_knowledge,-1
885,virtual,15,Loyalty Progression Rate (LPR),Measures how quickly a fan is accumulating loyalty within the system,"LPR = \left(\frac{loypts}{membdays}\right) \times (1 + (engrate \times 2)), \text{ showing points earned per day adjusted by engagement level to identify rapidly advancing fans.}",calculation_knowledge,-1
886,virtual,16,Fan Lifetime Value (FLV),Projects the total economic value of a fan throughout their relationship with the platform,"FLV = MV \times \left(1 - \frac{RRF}{10}\right) \times (1 + FEI) \times 24, \text{ estimating 24-month value adjusted by retention risk and engagement level.}",calculation_knowledge,"[10, 11, 13]"
887,virtual,17,Community Contribution Index (CCI),Measures a fan's overall contribution to the virtual idol community,"CCI = (CCIS \times 0.4) + (SIM \times 0.3) + (collabcount \times 0.1) + (FEI \times 0.2), \text{ balancing content creation, social influence, collaboration activity, and general engagement.}",calculation_knowledge,"[10, 12, 14]"
888,virtual,18,Event ROI Potential (ERP),Estimates the potential return on investment for inviting a fan to exclusive events,"ERP = FLV \times \left(\frac{evtpart\_numeric}{3}\right) \times \left(\frac{tierstep}{5}\right) \times \left(1 + \frac{inflscore}{100}\right), \text{ where evtpart\_numeric maps participation\_summary.event\_attendance.evtpart enum values: Never=0, Rare=1, Regular=2, Always=3. Higher scores indicate fans likely to generate more value when included in events.}",calculation_knowledge,[16]
889,virtual,19,Support Efficiency Index (SEI),Measures the efficiency of platform resources spent on supporting a fan,"SEI = \frac{satrate}{(supptix + 1)} \times \left(1 + \frac{FLV}{100}\right), \text{ where higher values indicate fans who provide good platform ratings with minimal support requirements, adjusted by their lifetime value.}",calculation_knowledge,[16]
890,virtual,20,Superfan,Identifies fans with exceptional platform value and engagement,"A fan with tierstep ≥ 8, FEI > 0.7, and MV > 200, representing the highest value segment providing substantial financial support while maintaining high engagement.",domain_knowledge,"[1, 10, 11]"
891,virtual,21,Content Creator,Identifies fans who actively produce and share idol-related content,"A fan with ugcval > 20, contqualrate > 7.0, and at least one of: artsubs > 3, ficsubs > 2, or coverperfcnt > 0. These fans contribute significantly to community content ecosystem.",domain_knowledge,[6]
892,virtual,22,Social Amplifier,Identifies fans who significantly extend idol content reach,"A fan with follcount > 500, SIM > 2.0, and viralcont ≥ 1, representing users who effectively spread idol content through their substantial social networks.",domain_knowledge,[14]
893,virtual,23,Churn Candidate,Identifies fans at immediate risk of platform abandonment,"A fan with RRF > 3.5, lastlogdt more than 20 days in the past, and engrate < 0.2, requiring immediate retention efforts.",domain_knowledge,"[5, 13]"
894,virtual,24,Silent Supporter,Identifies financially supportive fans with low social visibility,"A fan with MV > 100, engrate < 0.3, and chatmsg/sesscount ratio < 0.5, representing valuable economic contributors who prefer to observe rather than actively participate in community activities.",domain_knowledge,"[5, 11]"
895,virtual,25,Community Pillar,Identifies fans who form the foundation of the idol community,"A fan with CCI > 7, actfreq = 'Daily', membdays > 180, and community_engagement.group_involvement.grprole = 'Moderator' or community_engagement.group_involvement.grprole = 'Leader'. These fans play essential roles in maintaining community structure and culture.",domain_knowledge,[17]
896,virtual,26,Potential Ambassador,Identifies fans with high potential to represent the idol brand,"A fan with inflscore > 75, trustval > 8.5, FEI > 0.6, and contqualrate > 8.0, representing candidates for official ambassador programs who can authentically promote the idol.",domain_knowledge,"[6, 10]"
897,virtual,27,Whale,Identifies fans who provide extraordinary financial support,"A fan with giftvalusd > 500 or spendusd > 1000 within a 90-day period, regardless of other engagement metrics. These fans form the financial backbone of idol economic ecosystems.",domain_knowledge,[7]
898,virtual,28,Event Champion,Identifies fans who excel at event participation and promotion,"A fan with participation_summary.event_attendance.evtpart = 'Always', ERP > 50, and hashuse > 15, representing users who consistently attend events and actively promote them through social channels.",domain_knowledge,[18]
899,virtual,29,Multi-Idol Supporter,Identifies fans who support multiple virtual idols on the platform,"A fan who has interacted (interactfanpivot appears multiple times with different interactidolpivot values) with at least 2 different idols, with engrate > 0.4 for each. These fans spread their support across the platform ecosystem rather than focusing on a single idol.",domain_knowledge,[5]
900,virtual,30,Premium Engagement Ratio (PER),Measures the relationship between fan spending and their engagement level,"PER = \frac{MV}{FEI \times 100}, \text{ where higher values indicate fans who spend more relative to their engagement level.}",calculation_knowledge,"[10, 11]"
901,virtual,31,Content Quality to Engagement Ratio (CQER),Measures how a fan's content quality relates to their overall engagement,"CQER = \frac{contqualrate}{engrate \times 10}, \text{ where values above 1.0 indicate fans producing content of quality higher than their general engagement level would predict.}",calculation_knowledge,"[5, 6]"
902,virtual,32,Investment Recovery Period (IRP),Estimates how many months it will take to recover platform investment in a fan,"IRP = \frac{supptix \times 30}{MV \times (1 - \frac{RRF}{10})}, \text{ where supptix represents support tickets as a proxy for platform resources invested in the fan.}",calculation_knowledge,"[11, 13]"
903,virtual,33,Tier Acceleration Factor (TAF),Measures how quickly a fan is advancing through tier levels relative to platform norms,"TAF = \frac{tierstep}{\sqrt{membdays}} \times \frac{10}{\sqrt{365}}, \text{ normalized to annual scale for consistent comparison across fans with different membership durations.}",calculation_knowledge,[1]
904,virtual,34,Gift Impact Quotient (GIQ),Evaluates the relative impact of a fan's gifting behavior considering both volume and value,"GIQ = \frac{giftvalusd \times gifttot}{100}, \text{ producing an amplified measure of gifting significance by combining both quantity and monetary value.}",calculation_knowledge,[7]
905,virtual,35,Social Conversion Rate (SCR),Measures a fan's ability to convert their social network into active platform participants,"SCR = \frac{refcount}{(socialcommunity.community\_engagement->>'network.follcount')::int} \times 100, \text{ expressed as a percentage of followers successfully converted to platform users.}",calculation_knowledge,[14]
906,virtual,36,Content Quality Consistency (CQC),Measures the consistency of a fan's content quality relative to their engagement consistency,"CQC = contqualrate \times intconsist \times \frac{ugcval}{10}, \text{ where intconsist measures interaction consistency on a scale of 0-1.}",calculation_knowledge,[6]
907,virtual,37,Loyalty Value Ratio (LVR),Evaluates the relationship between a fan's loyalty points and their economic value,"LVR = \frac{loypts}{MV \times 10}, \text{ where higher values indicate fans accumulating loyalty faster than their spending would predict.}",calculation_knowledge,"[9, 11]"
908,virtual,38,Event Response Factor (ERF),Measures a fan's response rate to official events and activities,"ERF = \frac{(eventsandclub.participation\_summary->>'event\_attendance.onevtatt')::int + (eventsandclub.participation\_summary->>'event\_attendance.offevtatt')::int}{10} \times \left(1 + \frac{FEI}{2}\right), \text{ where onevtatt and offevtatt represent online and offline events attended.}",calculation_knowledge,[10]
909,virtual,39,Churn Prevention Investment (CPI),Calculates the optimal investment for preventing churn based on fan value and risk,"CPI = FLV \times \frac{RRF}{5} \times 0.1, \text{ suggesting investing up to 10\% of at-risk value, scaled according to churn risk level.}",calculation_knowledge,"[13, 16]"
910,virtual,40,High-Value Content Creator,Identifies fans who produce exceptional quality content with significant reach,"A fan who produces content with contqualrate > 8.5, has follcount > 1000, and maintains ugcval > 20, representing the elite tier of community content producers.",domain_knowledge,"[6, 21]"
911,virtual,41,Engagement-Deficient Whale,Identifies high-spending fans with surprisingly low engagement,"A fan with giftvalusd > 500 or spendusd > 1000, but with FEI < 0.3, indicating significant financial contribution despite limited platform participation.",domain_knowledge,"[10, 27]"
912,virtual,42,Rapidly Ascending Fan,Identifies fans who are progressing through membership tiers at an accelerated rate,"A fan with TAF > 1.5 and LPR > 20, demonstrating exceptional speed in advancing through tier levels and accumulating loyalty points.",domain_knowledge,"[1, 15, 33]"
913,virtual,43,Quality Inconsistent Creator,Identifies fans who produce occasional high-quality content but lack consistency,"A fan who has created at least one content piece with contqualrate > 8.5 but maintains an overall CQC < 5, indicating talent but inconsistent output.",domain_knowledge,"[6, 36]"
914,virtual,44,Retention Risk Superfan,Identifies high-value fans showing early warning signs of potential churn,"A fan with tierstep ≥ 8, FEI > 0.7, and MV > 200, but also showing RRF > 2.0, representing significant value at risk of being lost.",domain_knowledge,"[13, 20]"
915,virtual,45,Loyalty Underperformer,Identifies fans with unusually low loyalty points relative to their spending,"A fan with LVR < 0.5 and MV > 150, indicating a lower than expected loyalty point accumulation compared to their economic contribution.",domain_knowledge,"[11, 37]"
916,virtual,46,Premium Service Candidate,Identifies fans who would benefit from and likely pay for enhanced support services,"A fan with SEI < 0.8, MV > 300, and supptix > 5, representing users who require substantial support and have demonstrated significant economic value.",domain_knowledge,"[11, 19]"
917,virtual,47,Social Network Underutilizer,Identifies fans with large social networks but low conversion to platform activity,"A fan with follcount > 1000 but SCR < 0.5%, indicating a substantial but largely untapped potential for platform growth through their social connections.",domain_knowledge,"[14, 35]"
918,virtual,48,Tier-Stuck Veteran,Identifies long-term fans who have stalled in tier progression,"A fan with membdays > 365, engrate > 0.4, but TAF < 0.5, representing users who remain engaged but are advancing through tier levels more slowly than expected.",domain_knowledge,"[1, 5, 33]"
919,virtual,49,Gift-Focused Supporter,Identifies fans whose primary support comes through gifting rather than direct spending,"A fan with GIQ > 50 but spendusd < 100, showing a preference for gift-based support mechanisms over traditional subscription or purchase options.",domain_knowledge,"[7, 34]"
920,virtual,50,Content Creator Classification,"Categorizes fans based on their content creation quality, volume, and reach","Fans are classified into three categories based on content metrics: 'High-Value Content Creator' identifies fans with exceptional content quality (contqualrate > 8.5), significant reach (follcount > 1000), and substantial content volume (ugcval > 20); 'Content Creator' identifies fans who produce good quality content (contqualrate > 7.0) with substantial volume (ugcval > 20); 'Regular Fan' applies to all others who don't meet content creator thresholds, representing the standard user base who primarily consume rather than create content.",domain_knowledge,"[6, 21, 40]"
921,virtual,51,Content Preference Classification,Categorizes fans based on their primary content consumption preferences,"Uses the contpref field to classify viewers: contpref = 'Music' maps to 'Content Consumer (Music)', contpref = 'Dance' maps to 'Content Consumer (Dance)', contpref = 'Gaming' maps to 'Content Consumer (Gaming)', and all other values map to 'General Content Consumer'. This classification enables targeted content delivery and personalized marketing.",domain_knowledge,-1
922,virtual,52,Churn Risk Numeric Mapping,Converts the churn risk enum values to numeric form for analytical calculations,"Transforms the churnflag enum values into numeric equivalents for use in calculations and risk models: 'None' maps to 0 (representing minimal risk), 'Low' maps to 1 (representing slight risk), 'Medium' maps to 2 (representing moderate risk), and 'High' maps to 3 (representing severe risk). This numeric mapping enables mathematical operations in retention analysis formulas, especially for the Retention Risk Factor (RRF) calculation.",value_illustration,[8]
923,virtual,53,Fan Value Segmentation,Classifies fans into value tiers based on their Fan Lifetime Value (FLV) relative to platform-wide percentiles,"Segments fans into four distinct value categories using percentile thresholds: 'Top Tier' identifies fans with FLV above the 90th percentile (p90), representing the platform's most valuable users; 'High Value' identifies fans with FLV between the 75th and 90th percentiles (p75 to p90); 'Medium Value' identifies fans with FLV between the 50th and 75th percentiles (median to p75); 'Low Value' applies to all fans with FLV below the median (p50). This segmentation enables targeted retention strategies, premium service offerings, and resource allocation based on projected economic contribution.",domain_knowledge,[16]
924,virtual,54,Enhanced Churn Risk Severity Classification,Defines detailed categorization for high-risk churn candidates requiring intervention,Enhances the standard churn risk classification by adding a 'Severe' category for critically at-risk users. The classification uses precise RRF thresholds: 'Severe' identifies users with RRF > 4.5 requiring immediate intervention and executive attention; 'High' identifies users with RRF between 3.5-4.5 requiring prioritized retention campaigns; 'Medium' applies to users with RRF between 2.5-3.5 requiring standard monitoring. This refined classification enables more targeted allocation of retention resources based on urgency level and churn probability.,value_illustration,[13]
925,cybermarket,0,cybermarket|markets|mktclass,Explains the market classification system and its business implications,Market classes define the primary business model: 'Forum' platforms focus on information exchange and community interactions with minimal direct transactions; 'Service' markets specialize in offering digital services rather than goods; 'Marketplace' denotes traditional product-focused platforms with diverse inventory; 'Exchange' indicates financial transaction services predominantly featuring cryptocurrency trading.,value_illustration,-1
926,cybermarket,1,cybermarket|markets|sizecluster,Illustrates the significance of market size classification,"Size clusters represent market scale and reach: 'Small' markets typically have under 10,000 monthly users and limited vendor presence; 'Medium' markets host 10,000-50,000 monthly users with moderate vendor diversity; 'Large' markets serve 50,000-100,000 users with extensive product catalogs; 'Mega' markets exceed 100,000 monthly users with comprehensive vendor networks and represent the highest-risk monitoring targets.",value_illustration,-1
927,cybermarket,2,cybermarket|transactions|paymethod,Explains the significance of different payment methods,"Payment methods represent varying degrees of anonymity and traceability: 'Crypto_A' typically refers to Bitcoin, offering pseudonymous transactions with public ledgers; 'Crypto_B' often indicates Monero or similar privacy coins with enhanced transaction obfuscation; 'Crypto_C' represents emerging or niche cryptocurrencies; 'Token' indicates platform-specific value exchange systems that operate outside traditional blockchain networks.",value_illustration,-1
928,cybermarket,3,cybermarket|transactions|txstatus,Explains the transaction lifecycle stages and their implications,"'Pending' represents initiated but incomplete transactions awaiting verification or escrow conditions; 'Completed' indicates successfully executed transactions with all parties satisfied; 'Cancelled' denotes transactions terminated before completion, often requiring refund processing; 'Disputed' represents contested transactions requiring arbitration, indicating potential fraud or service failure.",value_illustration,-1
929,cybermarket,4,cybermarket|products|prodtheme,Clarifies product theme classifications and their enforcement implications,"'Digital' products include software, account credentials, and virtual goods requiring no physical shipping; 'Data' encompasses information packages like databases, personal information, and intellectual property; 'Service' refers to activities rather than tangible products, including hacking, documentation, or technical assistance; 'Physical' indicates tangible goods requiring actual shipment through delivery networks, representing the highest exposure risk category.",value_illustration,-1
930,cybermarket,5,cybermarket|communication|vpnflag,Explains the significance of VPN detection in communication analysis,"'Yes' indicates confirmed VPN usage, demonstrating deliberate attempts to mask true location and identity; 'No' suggests direct connections potentially revealing actual user locations; 'Suspected' indicates communication patterns consistent with VPN usage but lacking definitive confirmation, requiring further investigation to determine the true level of identity obfuscation.",value_illustration,-1
931,cybermarket,6,cybermarket|communication|langpattern,Explains the significance of language pattern classification,'Consistent' indicates uniform linguistic patterns suggesting single-user accounts with established communication habits; 'Variable' indicates significant linguistic variations that may signal multiple users sharing an account or automated translation tools; 'Suspicious' denotes deliberate attempts to mask writing style through syntax switching or unnatural language patterns often associated with deception attempts.,value_illustration,-1
932,cybermarket,7,cybermarket|vendors|vendchecklvl,Illustrates the vendor verification system and trustworthiness indicators,"'Basic' vendors have completed minimal verification steps, typically only email and captcha verification with limited platform history; 'Advanced' vendors have undergone additional verification including identification consistency checks and longer positive platform history; 'Premium' vendors represent the highest verification tier, having submitted verifiable identity elements and maintained extended positive transaction records with minimal disputes.",value_illustration,-1
933,cybermarket,8,cybermarket|riskanalysis|moneyrisk,Explains the significance of money laundering risk classification,"'Low' indicates minimal risk patterns with transparent transaction flows and consistent monetary behavior; 'Medium' suggests some unusual patterns warranting monitoring but insufficient evidence for immediate action; 'High' represents significant red flags such as rapid fund transfers, unusual transaction chains, or known high-risk wallet associations; 'Unknown' indicates insufficient data to properly assess risk, itself often considered a risk indicator due to potential deliberate obfuscation.",value_illustration,-1
934,cybermarket,9,cybermarket|securitymonitoring|alertsev,Explains the alert severity classification system,'Low' alerts indicate minor anomalies requiring minimal attention and posing limited security risk; 'Medium' alerts signal notable deviations from baseline behavior requiring investigation within standard timeframes; 'High' alerts denote significant security concerns demanding prompt attention and intervention; 'Critical' alerts represent severe and immediate security threats requiring urgent action and potential emergency response protocols.,value_illustration,-1
935,cybermarket,10,Market Risk Score (MRS),Calculates overall risk level of a market based on multiple factors,"MRS = \frac{dlyflow}{1000} + (esccomprate \times 0.2) + (interscore \times 0.3) + (vendcount \times 0.1) - \frac{mktspan}{100}, \text{where higher scores indicate greater risk exposure requiring enhanced monitoring.}",calculation_knowledge,-1
936,cybermarket,11,Vendor Trust Index (VTI),Measures vendor reliability based on transaction history,"VTI = \frac{vendsucccount}{vendtxcount} \times 100 - \frac{venddisputecount}{vendtxcount} \times 50 + (vendrate \times 5), \text{where higher scores indicate more trustworthy vendors.}",calculation_knowledge,-1
937,cybermarket,12,Transaction Anomaly Score (TAS),Detects unusual transactions based on multiple variables,"TAS = \frac{payamtusd}{1000} \times \frac{txfinishhrs}{24} \times \left(1 + \frac{escrowhrs}{100}\right) \times \left(1 - \frac{esccomprate}{100}\right), \text{where esccomprate is from the associated market, and higher scores indicate more suspicious transactions.}",calculation_knowledge,-1
938,cybermarket,13,Communication Security Risk (CSR),Evaluates the security risk of a communication channel,"CSR = (iptally \times 5) + (tornodecount \times 2) + (vpnflag\_numeric \times 30) + \frac{brwsrunique}{10} + (susppatscore \times 3) + (riskindiccount \times 4), \text{where vpnflag\_numeric maps Yes=1, Suspected=0.5, No=0, and higher scores indicate greater security concerns.}",calculation_knowledge,-1
939,cybermarket,14,Wallet Risk Index (WRI),Assesses the risk level of cryptocurrency wallets,"WRI = (fraudprob \times 100) + (wallrisksc \times 0.5) - \frac{wallage}{30} + (wallturnrt \times 10) + \frac{txvel}{10}, \text{where higher scores indicate potentially suspicious wallet activity.}",calculation_knowledge,-1
940,cybermarket,15,Market Stability Index (MSI),Measures the operational stability of a market,"MSI = \frac{mktspan}{365} \times \frac{esccomprate}{100} \times \left(1 - \frac{\sum venddisputecount}{\sum vendtxcount} \right) \times 100, \text{where higher scores indicate more stable markets less likely to disappear suddenly.}",calculation_knowledge,-1
941,cybermarket,16,Transaction Chain Risk (TCR),Evaluates the risk level of a transaction chain,"TCR = (txchainlen \times 10) + (linkedtxcount \times 5) + (fraudprob \times 100) - (profilecomplete \times 0.5) - (idverifyscore \times 0.5), \text{where higher scores indicate higher risk transaction chains.}",calculation_knowledge,-1
942,cybermarket,17,Security Posture Score (SPS),Evaluates the overall security posture of an entity,"SPS = (100 - (vulntally \times 5)) + (securitymeasurecount \times 2) + (sessionsecurityscore \times 0.5) + (privprotscore \times 0.3) - (fpprob \times 100), \text{where higher scores indicate stronger security postures.}",calculation_knowledge,-1
943,cybermarket,18,Investigation Priority Score (IPS),Determines how urgently an investigation should be handled,"IPS = (lawinterest\_numeric \times 30) + (regrisklvl\_numeric \times 20) + (fraudprob \times 100) - (compliancescore \times 0.5) + (notescount \times 2), \text{where lawinterest\_numeric and regrisklvl\_numeric map Low=1, Medium=2, High=3, Unknown=2, and higher scores indicate higher priority.}",calculation_knowledge,-1
944,cybermarket,19,Anonymity Protection Level (APL),Measures how well a user's identity is protected,"APL = (vpnflag\_numeric \times 30) + (tornodecount \times 2) + (encryptmethod\_numeric \times 15) + (connpatscore \times 0.2) + \frac{brwsrunique}{20}, \text{where vpnflag\_numeric maps Yes=1, Suspected=0.5, No=0, encryptmethod\_numeric maps Standard=1, Enhanced=2, Custom=3, else=0 and higher scores indicate stronger anonymity protections.}",calculation_knowledge,-1
945,cybermarket,20,High-Risk Market,Identifies markets with significant operational risk factors,"A market with MRS > 500, having more than 100 vendors, a daily flow exceeding 5000 transactions, and at least one 'High' security alert. These markets typically have the highest potential for illicit activity and represent priority monitoring targets for investigators.",domain_knowledge,[10]
946,cybermarket,21,Trusted Vendor,Identifies vendors with established positive reputation,"A vendor with VTI > 80, vendchecklvl of 'Advanced' or 'Premium', a dispute rate below 5% of total transactions, and an active history exceeding 90 days. These vendors represent stabilizing forces within markets and typically pose lower immediate enforcement priorities.",domain_knowledge,"[7, 11]"
947,cybermarket,22,Suspicious Transaction Pattern,Identifies transactions with characteristics suggesting potential illegal activity,"A transaction with TAS > 75, payment in privacy-focused cryptocurrencies (Crypto_B), escrow disabled or minimized (escrowused = 'No' or escrowhrs < 24), and unusual routing complexity (routecomplexity = 'Complex'). These transactions often represent high-risk activities requiring further investigation.",domain_knowledge,"[2, 12]"
948,cybermarket,23,Money Laundering Indicator,Identifies transaction patterns consistent with money laundering,"A transaction chain with TCR > 150, involving wallets less than 30 days old (wallage < 30), high turnover rates (wallturnrt > 5), and at least 3 linked transactions (linkedtxcount >= 3). These patterns often indicate attempts to obscure the source or destination of funds.",domain_knowledge,[16]
949,cybermarket,24,High-Security Entity,Identifies entities with strong security practices,"An entity with SPS > 80, using military-grade encryption, implementing 2FA or multi-factor authentication, and maintaining fewer than 5 vulnerabilities (vulntally < 5). These entities represent lower security breach risks but may indicate sophisticated operators requiring specialized investigation approaches.",domain_knowledge,[17]
950,cybermarket,25,Priority Investigation Target,Identifies cases requiring immediate investigative attention,"An investigation with IPS > 200, high law enforcement interest (lawinterest = 'High'), involving a suspicious transaction pattern, and connected to a high-risk market. These cases represent the highest priority for resource allocation and immediate intervention.",domain_knowledge,"[18, 20, 22]"
951,cybermarket,26,Identity-Protected User,Identifies users with robust anonymity protections,"A user with APL > 100, consistently using TOR (tornodecount > 20), employing VPN protection (vpnflag = 'Yes'), and utilizing custom encryption methods. These users demonstrate sophisticated operational security requiring specialized investigation techniques.",domain_knowledge,[19]
952,cybermarket,27,Market Migration Indicator,Identifies signs of users migrating between markets,"A pattern where multiple vendors (vendregistry) and buyers (buyregistry) associated with one market (mktregistry) begin appearing on another market within a short timeframe (less than 30 days), often following security incidents or market instability. These migrations typically indicate market disruption events requiring adjustments to monitoring priorities.",domain_knowledge,-1
953,cybermarket,28,Sophisticated Operational Security,Identifies entities employing advanced security practices,"An entity demonstrating APL > 120, consistently using variable language patterns (langpattern = 'Variable'), maintaining minimal communication (msgtally < 10), and employing multiple transaction chains (txchainlen > 5). These patterns indicate professional-level operational security possibly linking to organized criminal activity.",domain_knowledge,"[6, 19]"
954,cybermarket,29,Cross-Platform Operator,Identifies entities operating across multiple cybermarket platforms,"An entity identified through matching cryptographic or communication fingerprints (keymatchcount > 30) operating on three or more markets simultaneously, maintaining consistent security practices, and exhibiting similar transaction patterns across platforms. These operators represent higher-value intelligence targets due to their broader cybermarket ecosystem involvement.",domain_knowledge,-1
955,cybermarket,30,Market Vulnerability Index (MVI),Evaluates a market's susceptibility to disruption or shutdown,"MVI = (100 - MSI) + (COUNT(CASE WHEN alertsev IS NOT NULL THEN 1 END) / 10) \times (alertsev_numeric \times 2) - (vendcount \times 0.05) + (COUNT(CASE WHEN lawinterest = 'High' THEN 1 END) / 5), \text{where alertsev_numeric maps Low=1, Medium=2, High=3, Critical=4 as defined in the alert severity system, and higher scores indicate greater vulnerability to disruption.}",calculation_knowledge,"[9, 15]"
956,cybermarket,31,Vendor Network Centrality (VNC),Measures a vendor's connectedness within the market ecosystem,"VNC = (COUNT(DISTINCT mktref) \times 5) + \frac{vendtxcount}{50} + (VTI \times 0.1) - (1 - sizecluster_numeric) \times 10, \text{where sizecluster_numeric maps Small=1, Medium=2, Large=3, Mega=4 as described in the market size classification, and higher scores indicate more central market positioning.}",calculation_knowledge,"[1, 11]"
957,cybermarket,32,Product Risk Exposure (PRE),Quantifies the regulatory exposure risk associated with product listings,"PRE = prodtheme_weight + (escrowused_numeric \times 10) - \frac{escrowhrs}{24} + \frac{payamtusd}{500}, \text{where prodtheme_weight assigns Digital=10, Data=20, Service=30, Physical=50 based on the product theme classification, and escrowused_numeric is 0 if escrow is used and 1 if not.}",calculation_knowledge,[4]
958,cybermarket,33,Communication Pattern Risk (CPR),Evaluates how suspicious a communication pattern is based on multiple factors,"CPR = (langpattern_numeric \times 15) + (CSR \times 0.2) + (msgtally \times 0.5) - (1 - vpnflag_numeric) \times 20, \text{where langpattern_numeric maps Consistent=1, Variable=2, Suspicious=3 as explained in the language pattern classification, and higher scores represent more suspicious communication behavior.}",calculation_knowledge,"[6, 13]"
959,cybermarket,34,Transaction Velocity Metric (TVM),Measures the rapidity and volume of transactions from a single source,"TVM = \frac{COUNT(txregistry)}{(MAX(eventstamp) - MIN(eventstamp))} \times \frac{payamtusd}{500} \times (1 + (paymethod\_weight \times 0.1)), \text{where paymethod\_weight assigns Crypto\_A=1, Crypto\_B=3, Crypto\_C=2, Token=2 based on the payment method classifications defined in cybermarket|transactions|paymethod, and higher values indicate potentially suspicious transaction velocity.}",calculation_knowledge,[2]
960,cybermarket,35,Market Diversification Score (MDS),Evaluates the diversity of products and vendors within a market,"MDS = \frac{COUNT(DISTINCT prodsubcat)}{5} + \frac{vendcount}{50} + \frac{COUNT(txregistry)}{vendcount} \times 0.5 - \frac{mktclass\_weight}{10}, \text{where mktclass\_weight assigns Forum=1, Service=2, Marketplace=4, Exchange=3 as per the market classification system defined in cybermarket|markets|mktclass, and higher scores represent greater market diversification.}",calculation_knowledge,[0]
961,cybermarket,36,Operational Security Index (OSI),Quantifies an entity's operational security practices,"OSI = (APL \times 0.5) + (SPS \times 0.2) + (tornodecount \times 2) + (CASE WHEN encryptmethod = 'Standard' THEN 1 WHEN encryptmethod = 'Enhanced' THEN 2 WHEN encryptmethod = 'Custom' THEN 3 ELSE 0 END \times 5) - (iptally \times 0.5), \text{where APL and SPS are defined by the Anonymity Protection Level and Security Posture Score respectively, and higher scores indicate stronger operational security.}",calculation_knowledge,"[17, 19]"
962,cybermarket,37,Vendor Relationship Strength (VRS),Measures the strength of relationships between a vendor and their customers,"VRS = (vendrate \times 10) + \frac{vendsucccount}{vendtxcount} \times 50 + (vendchecklvl_numeric \times 15) - \frac{venddisputecount}{vendtxcount} \times 100, \text{where vendchecklvl_numeric maps Basic=1, Advanced=2, Premium=3, else=0 according to the vendor verification system, and higher scores represent stronger vendor-customer relationships.}",calculation_knowledge,[7]
963,cybermarket,38,Cross-Platform Risk Amplification (CPRA),Measures how risk increases when an entity operates across multiple platforms,"CPRA = (keymatchcount \times 3) + (COUNT(DISTINCT mktref) \times 10) + (WRI \times 0.2) + (\frac{mktspan}{365} \times 5) - (\frac{compliancescore}{20}), \text{where WRI is defined by the Wallet Risk Index, platformcount is the count of distinct markets an entity operates on, and higher values indicate greater cross-platform risk.}",calculation_knowledge,[14]
964,cybermarket,39,Money Flow Complexity (MFC),Quantifies the complexity of money flows in transaction chains,"MFC = (txchainlen \times 5) + (linkedtxcount \times 3) + (moneyrisk_numeric \times 15) + (TCR \times 0.2) - (profilecomplete \times 10), \text{where moneyrisk_numeric maps Low=1, Medium=2, High=3, Unknown=2 according to the money laundering risk classification, and higher scores indicate more complex money flows.}",calculation_knowledge,"[8, 16]"
965,cybermarket,40,Unstable Market,Identifies markets at high risk of imminent shutdown or disruption,"A market with MVI > 75, MSI < 40, and at least one 'Critical' security alert (alertsev = 'Critical'). These markets typically show signs of administrative instability, declining transaction volumes, and may suggest potential exit scam preparation or law enforcement attention.",domain_knowledge,"[9, 15, 30]"
966,cybermarket,41,Market Kingpin,Identifies vendors with exceptional influence and reach across multiple markets,"A vendor with VNC > 85, operating on at least 3 different markets (COUNT(DISTINCT mktref) >= 3), with 'Premium' verification level (vendchecklvl = 'Premium'), and displaying the characteristics of a Trusted Vendor. These operators represent significant intelligence targets due to their wide-reaching influence.",domain_knowledge,"[7, 21, 31]"
967,cybermarket,42,High-Exposure Product,Identifies products with elevated regulatory risk factors,"A product with PRE > 60, in the 'Physical' theme category as defined in the product theme classification, without escrow protection (escrowused = 'No'), and transacted with privacy-focused cryptocurrency (paymethod = 'Crypto_B'). These products require immediate monitoring due to their heightened risk profile.",domain_knowledge,"[2, 4, 32]"
968,cybermarket,43,Deceptive Communication Pattern,Identifies communication exhibiting signs of deliberate deception,"A communication with CPR > 70, 'Suspicious' language patterns as defined in the language pattern classification, high CSR values (CSR > 80), and frequently changing connection parameters. These patterns suggest deliberate attempts to obfuscate identity and intentions.",domain_knowledge,"[6, 13, 33]"
969,cybermarket,44,Flash Transaction Cluster,Identifies unusually rapid transaction sequences potentially indicating coordinated activity,"A group of transactions with TVM > 50 from related sources, using privacy-focused cryptocurrencies (paymethod = 'Crypto_B') as defined in the payment method classification, completed within a short timeframe (MAX(eventstamp) - MIN(eventstamp) < 24 hours), and involving minimal escrow time (escrowhrs < 12). Such clusters often indicate coordinated market manipulation or 'smurfing' behavior.",domain_knowledge,"[2, 34]"
970,cybermarket,45,Diversified Marketplace,Identifies markets with exceptional product and vendor diversity,"A market with MDS > 65, at least 15 distinct product categories (COUNT(DISTINCT prodsubcat) >= 15), high vendor count (vendcount > 200), and 'Marketplace' classification (mktclass = 'Marketplace') as defined in the market classification system. These markets typically present challenging enforcement targets due to their diversified nature.",domain_knowledge,"[0, 35]"
971,cybermarket,46,OpSec Specialist,Identifies entities employing exceptionally sophisticated operational security,"An entity with OSI > 85, demonstrating Sophisticated Operational Security characteristics, with exceptionally high APL scores (APL > 120), and using either 'Enhanced' or 'Custom' encryption methods (encryptmethod IN ('Enhanced', 'Custom')). These entities represent high-value intelligence targets requiring specialized investigation techniques.",domain_knowledge,"[19, 28, 36]"
972,cybermarket,47,Customer Loyalty Network,Identifies vendor-customer networks with unusual loyalty patterns,"A network centered on a vendor with VRS > 90, having repeated transactions with the same buyers (> 5 transactions per buyer), receiving exceptionally high ratings (vendrate > 4.8), and 'Advanced' or 'Premium' verification level as defined in the vendor verification system. These networks often indicate established trust circles warranting deeper investigation.",domain_knowledge,"[7, 37]"
973,cybermarket,48,Multi-Platform Threat Entity,Identifies high-risk entities operating across multiple cybermarket platforms,"An entity with CPRA > 80, displaying Cross-Platform Operator characteristics, with high Wallet Risk Index scores (WRI > 70), and consistently employing the same operational security tactics across platforms. These entities represent priority targets for coordinated investigation efforts due to their expanded reach.",domain_knowledge,"[14, 29, 38]"
974,cybermarket,49,Complex Money Laundering Operation,Identifies sophisticated financial obfuscation schemes,"A transaction network with MFC > 90, displaying Money Laundering Indicator characteristics, high TCR scores (TCR > 180), and 'High' money laundering risk classification as defined in the money laundering risk system. These operations represent the most sophisticated financial obfuscation attempts requiring specialized financial investigation approaches.",domain_knowledge,"[8, 16, 39]"
975,gaming,0,Sensor Performance Index (SPI),"A composite metric that evaluates overall sensor quality based on resolution, accuracy, and response time.","SPI = \frac{DpiRes}{1000} \times \left(1 - \frac{McRespTime}{10}\right) \times 10, \text{ where higher values indicate better overall sensor performance with balanced resolution and responsiveness.}",calculation_knowledge,-1
976,gaming,1,Battery Efficiency Ratio (BER),Measures how efficiently a device uses its battery capacity relative to its power draw under active conditions.,"BER = \frac{BattLifeH \times BattCapMah}{PwrActMw \times 10}, \text{ where higher values indicate more efficient power management and battery utilization.}",calculation_knowledge,-1
977,gaming,2,Input Responsiveness Score (IRS),"Quantifies the overall input responsiveness of a device considering polling rate, latency, and response time.","IRS = \frac{PollRateHz}{100} \times \left(1 - \frac{InpLagMs + RespTimeMs}{30}\right) \times 10, \text{ where higher values indicate more responsive input with minimal lag.}",calculation_knowledge,-1
978,gaming,3,Comfort Index (CI),Evaluates the ergonomic comfort of a device based on its physical design factors and ergonomic rating.,CI = \frac{ErgoRate}{10} \times \left(1 + \frac{(WristFlag ? 1 : 0)}{5}\right) \times \left(1 - \frac{|PalmAngle - 15|}{45}\right) \times 10,calculation_knowledge,-1
979,gaming,4,Audio Quality Index (AQI),"A comprehensive metric for evaluating audio device quality based on frequency response, distortion, and sensitivity.","AQI = \left(1 - \frac{ThdPct}{2}\right) \times \frac{SpkSenseDb}{100} \times \left(1 - \frac{AudLatMs}{100}\right) \times 10, \text{ where higher values indicate better overall audio quality with minimal distortion.}",calculation_knowledge,-1
980,gaming,5,Switch Performance Rating (SPR),"Rates mechanical switch performance based on durability, consistency, and tactile feedback.","SPR = \frac{\log_{10}(SwtchDur)}{7} \times SwtchCons \times \left(1 - \frac{KeyChatter}{2}\right), \text{ where higher values indicate better switch quality with longer lifespan and consistent performance.}",calculation_knowledge,-1
981,gaming,6,RGB Implementation Quality (RIQ),"Evaluates the quality of RGB implementation based on brightness, color accuracy, and lighting zones.","RIQ = \frac{RgbBright}{100} \times \frac{RgbColorAcc}{10} \times \left(0.5 + \frac{RgbZones}{20}\right), \text{ where higher values indicate premium RGB lighting with accurate colors and extensive customization options.}",calculation_knowledge,-1
982,gaming,7,Durability Score (DS),"Quantifies overall device durability based on material quality, environmental resistance, and impact protection.","DS = \left(\frac{DropHtM}{2} + \frac{BendForce}{100} + \frac{TwistDeg}{90}\right) \times \frac{UsbConnDur}{10000} \times 10, \text{ where higher values indicate more durable devices capable of withstanding physical stress.}",calculation_knowledge,-1
983,gaming,8,Wireless Performance Rating (WPR),"Rates the quality of wireless connectivity based on range, latency, and interference handling.","WPR = \frac{WlRangeM}{10} \times \left(1 - \frac{WlLatVar}{5}\right) \times \left(1 + \frac{WlChanHop}{2}\right) \times \frac{WlSignal + 100}{100}, \text{ where higher values indicate more reliable wireless performance with better range and stability.}",calculation_knowledge,-1
984,gaming,9,Gaming Device Value Index (GDVI),"A comprehensive metric evaluating overall gaming device value considering performance, durability, and comfort.","GDVI = \left(SPI \times 0.3\right) + \left(IRS \times 0.3\right) + \left(DS \times 0.2\right) + \left(CI \times 0.2\right), \text{ where higher values indicate better overall device quality across multiple dimensions.}",calculation_knowledge,"[0, 2, 7, 3]"
985,gaming,10,Premium Gaming Mouse,Defines the criteria for a high-end gaming mouse based on sensor performance and ergonomics.,"A mouse with SPI > 7.5, DpiRes ≥ 16000, PollRateHz ≥ 1000, and CI > 8.0, offering exceptional precision and comfort for competitive gaming scenarios.",domain_knowledge,"[0, 3]"
986,gaming,11,Tournament-Ready Keyboard,Defines what constitutes a keyboard suitable for competitive tournament play based on response time and switch quality.,"A keyboard with IRS > 8.5, ClkLat < 1.0ms, PollRateHz ≥ 1000, and SPR > 8.0, ensuring minimal input latency and consistent actuation during high-pressure competitive scenarios.",domain_knowledge,"[2, 5]"
987,gaming,12,Audiophile Gaming Headset,Defines the standards for premium audio quality in gaming headsets suitable for audiophiles.,"A headset with AQI > 8.0, FreqResp covering at least '10Hz-22kHz', ThdPct < 0.5%, and NoiseIsoDb > 15, delivering exceptional sound clarity, detail, and isolation for immersive gaming experiences.",domain_knowledge,[4]
988,gaming,13,Extended Battery Life Device,Identifies devices with exceptionally efficient battery performance for extended gaming sessions.,"A device with BER > 7.5, BattLifeH > 30, QChgFlag = true, and PwrIdleMw < 100, enabling marathon gaming sessions with minimal charging interruptions.",domain_knowledge,[1]
989,gaming,14,Professional Esports Controller,Defines the quality standards for controllers used in professional esports competitions.,"A controller with IRS > 8.0, JoyPrec > 9.0, DriftRes > 9.5, TrigRes ≥ 5, and HapStr > 8, offering precise inputs and reliable performance required for competitive play at the highest level.",domain_knowledge,[2]
990,gaming,15,Streaming-Optimized Device,Identifies devices specifically designed for content creators and streamers with appropriate features.,"A device with MicSenseDb < -45, MicFreqResp covering at least '50Hz-18kHz', McRespTime < 1.0, and ProfCount ≥ 5, allowing streamers to capture high-quality audio while maintaining flexible device configurations.",domain_knowledge,-1
991,gaming,16,Competitive-Grade Durability,Defines durability standards for devices intended for intensive competitive use.,"A device with DS > 8.5, UsbConnDur > 15000, DropHtM > 1.5, and GripDur > 1000, designed to withstand the rigors of frequent transport and intensive use during tournament environments.",domain_knowledge,[7]
992,gaming,17,Minimal Input Latency,Identifies devices with exceptionally low input latency suitable for reaction-critical games.,"A device with InpLagMs < 1.0, PollRateHz ≥ 1000, LatMs < 2.0, and ClkLat < 0.8, providing almost instantaneous input recognition critical for competitive FPS and fighting games.",domain_knowledge,-1
993,gaming,18,Premium Wireless Solution,Defines high-end wireless gaming devices with performance comparable to wired alternatives.,"A device with WPR > 9.0, ConnType = 'Wireless 2.4GHz', LatMs < 2.5, and BattLifeH > 24, eliminating common wireless drawbacks while maintaining the convenience of cable-free operation.",domain_knowledge,[8]
994,gaming,19,Full-Featured Gaming Setup,Identifies comprehensive gaming setups combining multiple high-quality devices with complementary features.,"A collection of devices where the average GDVI > 8.5 across at least three different device categories (Mouse, Keyboard, Headset, etc.), providing a complete and cohesive premium gaming experience.",domain_knowledge,[9]
995,gaming,20,LatMs (Latency),Illustrates the significance of latency measurements in gaming peripherals for competitive play.,"Measured in milliseconds, representing the delay between user input and system response. Values below 1.0ms enable split-second reactions in fast-paced competitive games, while values above 5.0ms can create noticeable input delay that impacts performance in timing-critical scenarios.",value_illustration,-1
996,gaming,21,PollRateHz (Polling Rate),Illustrates the importance of polling rate in gaming device responsiveness.,"Measured in Hertz (times per second), representing how often the device reports its state to the computer. Values of 125Hz are standard for office peripherals, 500Hz is good for casual gaming, 1000Hz is preferred for competitive play, and 4000Hz+ represents cutting-edge technology offering ultra-responsive input for professional esports.",value_illustration,-1
997,gaming,22,DpiRes (DPI Resolution),Illustrates the impact of DPI resolution settings on mouse sensitivity and precision.,"Measured in dots per inch, representing sensor tracking precision. Values around 800-1600 are typically used by professional FPS players for precision, while 12000+ allows for extremely fast cursor movement with minimal physical motion, beneficial for MOBAs and strategy games that require covering large screen areas quickly.",value_illustration,-1
998,gaming,23,SwtchDur (Switch Durability),Illustrates the significance of switch durability ratings in keyboard longevity.,"Measured in actuation cycles before failure. Values around 20 million indicate consumer-grade mechanical switches, 50 million represent premium mechanical switches for enthusiasts, and 100+ million typically denote optical or magnetic switches designed for extended competitive use with minimal degradation over time.",value_illustration,-1
999,gaming,24,ErgoRate (Ergonomic Rating),Illustrates the significance of ergonomic ratings in gaming peripherals for comfort during extended use.,"Rated on a scale of 1-10, where 1-3 indicates basic designs prioritizing aesthetics over comfort, 4-6 represents good ergonomics for average gaming sessions, 7-8 denotes devices designed for extended comfort, and 9-10 indicates specialized ergonomic designs preventing repetitive strain injuries during marathon sessions.",value_illustration,-1
1000,gaming,25,WlSignal (Wireless Signal Strength),Illustrates the importance of wireless signal strength measurements for stable connectivity.,"Typically measured in dBm, with values ranging from -30 (excellent) to -90 (poor). Signal strength above -50dBm indicates optimal performance with minimal interference, -50 to -70dBm represents good connectivity suitable for most gaming, while below -70dBm may experience occasional disconnections or increased latency in challenging environments.",value_illustration,-1
1001,gaming,26,ThdPct (Total Harmonic Distortion),Illustrates the impact of harmonic distortion measurements on audio quality in gaming headsets.,"Measured as a percentage, representing audio signal distortion at high volumes. Values below 0.1% indicate audiophile-grade reproduction with imperceptible distortion, 0.1-0.5% represents excellent gaming audio quality, 0.5-1.0% is acceptable for most gaming scenarios, while values above 1.0% may produce noticeable distortion during intense gaming sequences.",value_illustration,-1
1002,gaming,27,DriftRes (Drift Resistance),Illustrates the significance of drift resistance in analog sticks for consistent gaming performance.,"Rated on a scale of 1-10, where values below 5 indicate susceptibility to developing drift over time, 6-8 represents good resistance to drift under normal use conditions, and 9-10 indicates premium Hall Effect or magnetic sensing technologies specifically designed to eliminate drift entirely throughout the controller's lifespan.",value_illustration,-1
1003,gaming,28,RgbZones (RGB Lighting Zones),Illustrates the customization potential of RGB lighting zones in gaming peripherals.,"Measured as distinct independently controllable lighting areas. Single-zone devices offer basic lighting effects, 3-5 zones allow for moderate customization like directional effects, 10+ zones enable complex patterns and game-reactive lighting, while per-key RGB (often 100+ zones) provides maximum customization with unique effects for individual keys or areas.",value_illustration,-1
1004,gaming,29,BrdMemMB (Onboard Memory),Illustrates the significance of onboard memory capacity for storing gaming profiles and settings.,"Measured in megabytes, representing storage for device configurations. Values of 1MB typically store 1-3 basic profiles with limited macros, 4-8MB allows for 5+ detailed profiles with complex macros and lighting effects, while 16MB+ enables tournament players to store numerous game-specific configurations with extensive customization independent of software.",value_illustration,-1
1005,gaming,30,Competitive Gaming Performance Index (CGPI),"A comprehensive metric evaluating a device's suitability for competitive gaming based on response time, accuracy, and durability.",CGPI = \left(IRS \times 0.4\right) + \left(SPI \times 0.3\right) + \left(SPR \times 0.2\right) + \left(RAI \times 0.1\right),calculation_knowledge,"[2, 0, 5, 31]"
1006,gaming,31,Response Accuracy Index (RAI),"Measures the accuracy and consistency of input response in gaming devices, accounting for both sensor precision and control stability.",RAI = \left(\frac{JoyPrec + DpadAcc}{20}\right) \times \left(1 - \frac{InpLagMs}{10}\right) \times 10,calculation_knowledge,-1
1007,gaming,32,Ergonomic Sustainability Factor (ESF),Evaluates how suitable a device is for extended gaming sessions based on ergonomic design and comfort metrics.,ESF = CI \times \left(1 + \frac{ErgoRate - 5}{10}\right) \times \left(1 - \frac{|PalmAngle - 15|}{30}\right) \times \left(1 + \frac{ErgoRate \times WristFlag}{50}\right),calculation_knowledge,[3]
1008,gaming,33,Immersion Enhancement Coefficient (IEC),"Quantifies how well a device contributes to gaming immersion through audio quality, haptic feedback, and visual elements.",IEC = \left(AQI \times 0.5\right) + \left(HFQ \times 0.3\right) + \left(RIQ \times 0.2\right),calculation_knowledge,"[4, 34, 6]"
1009,gaming,34,Haptic Feedback Quality (HFQ),Measures the quality and effectiveness of haptic feedback systems in gaming controllers and devices.,HFQ = \left(\frac{HapStr}{10}\right) \times \left(1 + \frac{VibModes}{10}\right) \times \left(1 + \frac{ForceFeed\_length}{20}\right),calculation_knowledge,-1
1010,gaming,35,Wireless Performance Efficiency (WPE),Evaluates the efficiency of wireless performance relative to battery consumption for untethered gaming devices.,WPE = WPR \times \sqrt{\frac{BER}{5}} \times \left(1 - \frac{WlLatVar}{3}\right) \times 2,calculation_knowledge,"[8, 1]"
1011,gaming,36,Gaming Versatility Score (GVS),Assesses a device's versatility across different gaming genres and use cases based on adaptability and feature set.,GVS = \frac{ProfCount + 1}{3} \times \left(CGPI \times 0.6\right) + \left(IEC \times 0.4\right),calculation_knowledge,"[30, 33]"
1012,gaming,37,Physical Endurance Rating (PER),"Measures the physical endurance of a device under intense gaming conditions, combining durability with effective heat management. ","PER = DS \times \left(1 + \frac{DustRes score + WaterRes score}{6}\right) \times \left(1 - \frac{100 - BendForce}{200}\right), where DustRes and WaterRes are scored based on IP ratings (IPX0=0, IPX1=1, IPX2=2, IPX3=3).",calculation_knowledge,[7]
1013,gaming,38,Professional Adoption Rating (PAR),Quantifies the level of professional gamer adoption and tournament presence of a device based on performance metrics and pro-level features.,PAR = \frac{CGPI}{10} \times \left(1 + \frac{ProfCount}{5}\right) \times \left(\frac{SPI + IRS}{15}\right),calculation_knowledge,"[30, 0, 2]"
1014,gaming,39,Value Proposition Index (VPI),"A comprehensive metric evaluating a gaming device's overall value by balancing performance, durability, ergonomics, and professional adoption.",VPI = \left(GDVI \times 0.4\right) + \left(ESF \times 0.3\right) + \left(PER \times 0.2\right) + \left(PAR \times 0.1\right),calculation_knowledge,"[9, 32, 37, 38]"
1015,gaming,40,Tournament Standard Device,Defines the minimum requirements for devices used in formal esports tournaments and professional competitions.,"A device that meets the CGPI > 8.0, LatMs < 2.0, PollRateHz ≥ 1000, and WlLatVar < 1.0 if wireless, supporting the precision and reliability demands of tournament play with consistent performance across extended match durations.",domain_knowledge,[30]
1016,gaming,41,Ergonomic Excellence Certification,Designates devices specifically designed to prevent repetitive strain injuries and support extended professional gaming sessions.,"A device with ESF > 8.5, ErgoRate > 8, CI > 8.5, and at least one specialized ergonomic feature (WristFlag = true or PalmAngle between 10-20°), designed to maintain player comfort and prevent strain during marathon gaming sessions.",domain_knowledge,"[32, 3]"
1017,gaming,42,Professional Multi-Genre Setup,Identifies device configurations specifically optimized for players who compete across multiple game genres at a high level.,"A device or device set with GVS > 8.5, ProfCount ≥ 4, and CGPI > 7.5, providing the adaptability and precision required by players who compete across FPS, MOBA, RTS, and other competitive genres.",domain_knowledge,"[36, 30]"
1018,gaming,43,Ultra-Responsive Gaming Device,Defines the elite tier of input devices with exceptional response characteristics for reaction-critical competitive games.,"A device with IRS > 9.0, RAI > 8.5, LatMs < 1.0, and ClkLat < 0.5, delivering near-instantaneous input recognition essential for competitive gaming at the highest levels where milliseconds determine outcomes.",domain_knowledge,"[2, 31]"
1019,gaming,44,Premium Immersive Experience Device,Identifies devices specifically optimized to enhance gaming immersion through superior sensory feedback.,"A device with IEC > 8.0, AQI > 7.5, HFQ > 8.0 if applicable, and RIQ > 7.0 if featuring lighting, designed to maximize player immersion in atmospheric and narrative-driven games.",domain_knowledge,"[33, 4, 34, 6]"
1020,gaming,45,Extended Tournament Ready Wireless,Defines wireless devices suitable for full-day tournament use without connectivity or battery concerns.,"A wireless device with WPE > 8.5, BER > 7.0, BattLifeH > 40, and LatMs < 2.5, providing reliable, tournament-grade performance throughout extended competition days without requiring recharging or experiencing degraded responsiveness.",domain_knowledge,"[35, 1]"
1021,gaming,46,Professional-Grade Control Consistency,Identifies controllers and input devices with exceptionally consistent control characteristics required for high-level competitive play.,"A device with RAI > 8.5, SwtchCons > 9.0, JoyPrec > 9.0 (if applicable), and DriftRes > 9.0 (if featuring analog sticks), delivering the precise, predictable input response professional players rely on for muscle memory development and consistent performance across practice and tournament environments.",domain_knowledge,[31]
1022,gaming,47,Ultra-Durable Tournament Device,Defines devices with exceptional physical durability designed to withstand the rigors of frequent tournament travel and intensive competition use.,"A device with PER > 9.0, DS > 8.5, UsbConnDur > 20000, and at least one premium durability feature (DropHtM > 2.0 or WaterRes containing 'IPX7' or higher), engineered to maintain performance integrity despite frequent transportation, setup/teardown cycles, and competition intensity.",domain_knowledge,"[37, 7]"
1023,gaming,48,Pro-Player Performance Certified,Identifies devices that have been validated through professional player testing and competition use at the highest levels.,"A device with PAR > 8.5, CGPI > 8.0, and specialized features matching competitive requirements for at least two major esports titles, representing equipment that has proven its performance capabilities in professional tournament environments.",domain_knowledge,"[38, 30]"
1024,gaming,49,Elite Gaming Ecosystem,Identifies comprehensive device ecosystems that deliver exceptional performance across all gaming interaction points for competitive players.,"A multi-device setup where each component achieves VPI > 8.5, with an average GDVI > 9.0 across at least three different device categories, providing seamless integration and consistent high performance across all player-game interaction points to maximize competitive advantage.",domain_knowledge,"[39, 9]"
1025,gaming,50,Battery Efficiency Classification,A categorical framework for evaluating and classifying the battery efficiency of gaming devices based on their Battery Efficiency Ratio (BER).,"A classification system that segments wireless gaming devices into four efficiency categories: 'Excellent Efficiency' (BER > 7.5) indicating marathon gaming capability, 'Good Efficiency' (BER between 5.0 and 7.5) for extended gaming sessions, 'Average Efficiency' (BER between 2.5 and 4.9) suitable for regular gameplay, and 'Poor Efficiency' (BER < 2.5) requiring frequent recharging, providing standardized comparison metrics across different gaming peripherals.",domain_knowledge,[1]
1026,gaming,51,RGB Quality Classification,A systematic framework for categorizing the quality of RGB lighting implementations in gaming peripherals based on the RGB Implementation Quality (RIQ) score.,"A classification system that segments gaming devices into four RGB quality categories: 'Premium RGB Implementation' (RIQ > 8.0) indicating exceptional color accuracy and customization capabilities, 'High-Quality RGB' (RIQ between 6.0 and 8.0) for advanced lighting effects with good color reproduction, 'Standard RGB' (RIQ between 3.0 and 5.9) suitable for basic customization needs, and 'Basic RGB' (RIQ < 3.0) offering limited lighting features, providing standardized comparison metrics for RGB lighting quality across gaming peripherals.",domain_knowledge,[6]
1027,gaming,52,Subpar Audio Device Identification,Identifies audio devices that fail to meet audiophile gaming standards and need improvement or removal from premium product lines,"Audio devices with one or more critical quality deficiencies: AQI score of 8.0 or lower, total harmonic distortion (ThdPct) of 0.5% or higher, noise isolation (NoiseIsoDb) of 15dB or lower, or frequency response not covering the full 10Hz-22kHz range required for immersive gaming audio experiences. These devices are candidates for improvement or reclassification to non-audiophile product categories.",domain_knowledge,"[4, 12]"
1028,gaming,53,Global Efficiency Percentile (GEP),Ranks a device’s Battery Efficiency Ratio (BER) relative to all other wireless gaming devices.,"GEP = \text{PERCENT\_RANK}()_{BER} \times 100, where PERCENT\_RANK() is computed over all devices sorted by BER; 0\% corresponds to the lowest BER and 100\% to the highest.",calculation_knowledge,[1]
1029,crypto,0,Spread Percentage,Calculates the spread as a percentage of the midpoint price.,"Spread Percentage = \frac{askquote - bidquote}{midquote} \times 100, \text{where } askquote \text{ is the best ask price, } bidquote \text{ is the best bid price, and } midquote \text{ is the midpoint price.}",calculation_knowledge,-1
1030,crypto,1,Slippage Impact,Calculates the expected price slippage impact for a given order size.,"Slippage Impact = \frac{dealcount}{bidunits \text{ or } askunits} \times spreadband, \text{where } dealcount \text{ is the order quantity, } bidunits/askunits \text{ is the quantity available at best bid/ask, and } spreadband \text{ is the raw spread.}",calculation_knowledge,-1
1031,crypto,2,Position Value at Risk (PVaR),Calculates the value at risk for a position based on current market conditions.,"PVaR = possum \times volmeter \times 0.01, \text{where } possum \text{ is the notional value of position and } volmeter \text{ is the volatility or fluctuation rating.}",calculation_knowledge,-1
1032,crypto,3,Arbitrage Opportunity Score (AOS),Quantifies the potential arbitrage opportunity considering multiple factors.,"AOS = arbpotential + xexchband + (fundgap \times 2) + basisgap, \text{where these components represent different types of arbitrage opportunities.}",calculation_knowledge,-1
1033,crypto,4,Market Impact Cost (MIC),Estimates the market impact cost of executing a large order.,"MIC = dealcount \times dealquote \times mkteffect \times 0.01, \text{where } dealcount \text{ is the order quantity, } dealquote \text{ is the limit or stop price, and } mkteffect \text{ is the approximating internal market impact.}",calculation_knowledge,-1
1034,crypto,5,Liquidity Ratio,Measures the ratio of available liquidity to total market volume.,"Liquidity Ratio = \frac{(bidunits + askunits) \times midquote}{volday}, \text{where } bidunits \text{ and } askunits \text{ are quantities at best bid and ask, } midquote \text{ is the midpoint price, and } volday \text{ is the 24h volume.}",calculation_knowledge,-1
1035,crypto,6,Realized Risk Ratio (RRR),Calculates the ratio of realized PnL to position value at risk.,"RRR = \frac{realline}{PVaR}, \text{where } realline \text{ is the realized PnL and } PVaR \text{ is the Position Value at Risk.}",calculation_knowledge,[2]
1036,crypto,7,Margin Utilization,Calculates the percentage of margin being utilized.,"Margin Utilization = \frac{inithold}{margsum} \times 100, \text{where } inithold \text{ is the initial margin required and } margsum \text{ is the margin account balance.}",calculation_knowledge,-1
1037,crypto,8,Order Fill Rate,Calculates the percentage of an order that has been filled.,"Order Fill Rate = \frac{dealcount - remaincount}{dealcount} \times 100, \text{where } dealcount \text{ is the order quantity and } remaincount \text{ is how many units remain unfilled.}",calculation_knowledge,-1
1038,crypto,9,Market Efficiency Ratio (MER),Measures how efficiently orders are executed compared to expected slippage.,"MER = \frac{Slippage Impact}{slipratio}, \text{where } Slippage Impact \text{ is the calculated expected slippage and } slipratio \text{ is the average slippage measure.}",calculation_knowledge,[1]
1039,crypto,10,Whale Order,Identifies large orders that could significantly impact market prices.,An order where the dealcount exceeds 10% of the available liquidity (bidunits or askunits) at the current best bid or ask price.,domain_knowledge,-1
1040,crypto,11,Liquidation Risk Level,Categorizes positions based on their proximity to liquidation.,"Positions are categorized as 'Safe', 'Moderate', or 'High Risk' based on how close the current market price is to the liqquote (liquidation price). A position is 'High Risk' when market price is within 5% of liqquote.",domain_knowledge,-1
1041,crypto,12,Arbitrage Window,Identifies time periods with significant arbitrage opportunities across markets.,"A market condition where the Arbitrage Opportunity Score exceeds 0.05, indicating substantial price discrepancies that can be exploited.",domain_knowledge,[3]
1042,crypto,13,Over-Leveraged Position,Identifies positions with excessive leverage relative to market volatility.,"A position where the leverage (posmagn) multiplied by the volatility measure (volmeter) exceeds 500, indicating high risk exposure.",domain_knowledge,-1
1043,crypto,14,Market Maker Activity,Identifies periods of high market maker participation.,"Market conditions where exectune is predominantly 'Maker' and makermotion is 'High', indicating strong liquidity provision by market makers.",domain_knowledge,-1
1044,crypto,15,Smart Money Flow,Identifies directional bias of sophisticated traders.,"Market conditions where smartforce exceeds both retailflow and instflow by at least 20%, indicating strong directional bias from sophisticated traders.",domain_knowledge,-1
1045,crypto,16,Liquidity Crisis,Identifies periods of severely reduced market liquidity.,"Market conditions where the Liquidity Ratio falls below 0.01, indicating insufficient market depth relative to typical trading volume.",domain_knowledge,[5]
1046,crypto,17,Momentum Divergence,Identifies when price action diverges from momentum indicators.,"Market condition where price makes new highs/lows while momentum indicators (buyforce, sellforce) move in the opposite direction.",domain_knowledge,-1
1047,crypto,18,Margin Call Risk,Identifies accounts at risk of receiving a margin call.,"Accounts where the Margin Utilization exceeds 80%, putting them at risk of margin calls if market prices move adversely.",domain_knowledge,[7]
1048,crypto,19,Technical Breakout,Identifies when price breaks significant technical levels with volume.,Market condition where price exceeds the highspotday or falls below lowspotday with volume (volday) at least 50% above the 30-day average.,domain_knowledge,-1
1049,crypto,20,dealedge,Illustrates the meaning of different values for the dealedge enum.,"An enum with values 'Buy' or 'Sell'. 'Buy' indicates the order is to purchase the base asset using the quote asset (e.g., buying BTC with USDT). 'Sell' indicates the order is to sell the base asset for the quote asset (e.g., selling BTC for USDT).",value_illustration,-1
1050,crypto,21,orderflow,Illustrates the meaning of different values for the orderflow enum.,"An enum with values 'New', 'PartiallyFilled', 'Cancelled', or 'Filled'. 'New' indicates a newly placed order that hasn't been matched. 'PartiallyFilled' means some portion has been executed but not all. 'Cancelled' means the order was cancelled before full execution. 'Filled' means the order has been completely executed.",value_illustration,-1
1051,crypto,22,timespan,Illustrates the meaning of different values for the timespan enum.,"An enum with values 'IOC', 'GTC', 'GTD', or 'FOK'. 'IOC' (Immediate-or-Cancel) means execute immediately available portion or cancel. 'GTC' (Good-Till-Cancelled) means the order remains active until explicitly cancelled. 'GTD' (Good-Till-Date) means the order remains active until a specified date. 'FOK' (Fill-or-Kill) means execute completely immediately or cancel entirely.",value_illustration,-1
1052,crypto,23,posedge,Illustrates the meaning of different values for the posedge enum.,An enum with values 'Long' or 'Short'. 'Long' indicates a position that profits from price increases of the underlying asset. 'Short' indicates a position that profits from price decreases of the underlying asset.,value_illustration,-1
1053,crypto,24,posmagn,Illustrates the meaning of different values for the posmagn enum.,"An enum with values '1', '2', '3', '5', '10', '20', '50', or '100', representing leverage multipliers. For example, '10' means the position uses 10x leverage, amplifying both potential profits and losses by a factor of 10 compared to an unleveraged position.",value_illustration,-1
1054,crypto,25,mktfeel,Illustrates the meaning of different values for the mktfeel enum.,"An enum with values 'Bearish', 'Bullish', or 'Neutral'. 'Bearish' indicates negative market sentiment with expectations of price decreases. 'Bullish' indicates positive market sentiment with expectations of price increases. 'Neutral' indicates balanced market sentiment with no strong directional bias.",value_illustration,-1
1055,crypto,26,techmeter,Illustrates the meaning of different values for the techmeter enum.,"An enum with values 'Buy', 'Sell', or 'Hold'. 'Buy' indicates technical indicators suggest purchasing the asset. 'Sell' indicates technical indicators suggest selling the asset. 'Hold' indicates technical indicators suggest maintaining current positions without new trades.",value_illustration,-1
1056,crypto,27,whalemotion,Illustrates the meaning of different values for the whalemotion enum.,"An enum with values 'Low', 'Medium', or 'High'. 'Low' indicates minimal activity from large traders. 'Medium' indicates moderate activity from large traders. 'High' indicates significant activity from large traders, potentially signaling important market movements.",value_illustration,-1
1057,crypto,28,makermotion,Illustrates the meaning of different values for the makermotion enum.,"An enum with values 'Low', 'Medium', or 'High'. 'Low' indicates minimal market maker activity with potentially wider spreads. 'Medium' indicates normal market maker activity. 'High' indicates substantial market maker activity, typically resulting in tighter spreads and higher liquidity.",value_illustration,-1
1058,crypto,29,exectune,Illustrates the meaning of different values for the exectune enum.,An enum with values 'Maker' or 'Taker'. 'Maker' indicates the order added liquidity to the order book by not matching immediately. 'Taker' indicates the order removed liquidity from the order book by matching with existing orders immediately upon placement.,value_illustration,-1
1059,crypto,30,Risk-Adjusted Return,Calculates return on position adjusted for risk exposure.,"Risk-Adjusted Return = \frac{realline}{PVaR \times posriskrate}, \text{where } realline \text{ is the realized PnL, } PVaR \text{ is the Position Value at Risk, and } posriskrate \text{ is the position risk ratio.}",calculation_knowledge,"[2, 6]"
1060,crypto,31,True Cost of Execution,Calculates the total cost of order execution including fees and slippage.,"True Cost of Execution = feetotal + (dealcount \times dealquote \times Slippage Impact \times 0.01), \text{where } feetotal \text{ is the total fee charged and } Slippage Impact \text{ is the expected price slippage impact for the order size.}",calculation_knowledge,[1]
1061,crypto,32,Order Book Imbalance Ratio,Quantifies the imbalance between bid and ask sides of the order book.,"Order Book Imbalance Ratio = \frac{biddepth - askdepth}{biddepth + askdepth}, \text{where } biddepth \text{ is the deeper bid liquidity and } askdepth \text{ is the deeper ask liquidity. A positive Imbalance Ratio indicates stronger buying pressure, while negative indicates stronger selling pressure.",calculation_knowledge,[5]
1062,crypto,33,Effective Leverage,Calculates the actual leverage considering both explicit leverage setting and position size relative to account balance.,"Effective Leverage = posmagn \times \frac{possum}{walletsum}, \text{where } posmagn \text{ is the position leverage, } possum \text{ is the notional value of position, and } walletsum \text{ is the total wallet balance.}",calculation_knowledge,-1
1063,crypto,34,Profit Factor,Measures the ratio of profitable trades to losing trades adjusted for their values.,"Profit Factor = \frac{\sum positive\ realline}{|\sum negative\ realline|}, \text{where } realline \text{ is the realized PnL, calculated separately for positive and negative values.}",calculation_knowledge,[6]
1064,crypto,35,Arbitrage ROI,Calculates the potential return on investment for an arbitrage opportunity.,"Arbitrage ROI = \frac{AOS \times dealquote}{feetotal \times 2}, \text{where } AOS \text{ is the Arbitrage Opportunity Score and } feetotal \text{ is multiplied by 2 to account for fees on both transactions involved in arbitrage.}",calculation_knowledge,"[3, 31]"
1065,crypto,36,Market Depth Ratio,Measures the ratio of order book depth to position size to assess market liquidity for position exit.,"Market Depth Ratio = \frac{biddepth \text{ or } askdepth}{dealcount} \times Liquidity Ratio, \text{where } biddepth/askdepth \text{ is used depending on position direction (posedge), } dealcount \text{ is the order quantity, and } Liquidity Ratio \text{ measures available liquidity to total market volume.}",calculation_knowledge,"[5, 8]"
1066,crypto,37,Volatility-Adjusted Spread,Normalizes the spread by the market volatility to determine if spread is wide relative to expected price movement.,"Volatility-Adjusted Spread = \frac{Spread Percentage}{volmeter \times 0.1}, \text{where } Spread Percentage \text{ is the spread as percentage of midpoint price and } volmeter \text{ is the volatility or fluctuation rating.}",calculation_knowledge,[0]
1067,crypto,38,Risk-to-Reward Ratio,Calculates the ratio of potential risk to potential reward for a position.,"Risk-to-Reward Ratio = \frac{|entryquote - (posedge == 'Long' ? stopquote : trigquote)|}{|entryquote - (posedge == 'Long' ? trigquote : stopquote)|}, \text{where } entryquote \text{ is the entry price, } stopquote \text{ is the stop price, } trigquote \text{ is the advanced trigger price, and } posedge \text{ determines position direction (Long or Short).}",calculation_knowledge,"[9, 23]"
1068,crypto,39,Technical Signal Strength,Quantifies the strength of technical signals based on multiple indicators.,"Technical Signal Strength = \frac{|rsi14spot - 50| + |macdtrail| + (bbandspan \times 0.01)}{3} \times (techmeter == 'Buy' ? 1 : techmeter == 'Sell' ? -1 : 0), \text{where } rsi14spot \text{ is the RSI indicator, } macdtrail \text{ is the MACD line, } bbandspan \text{ is the Bollinger Band width, and } techmeter \text{ determines direction (Buy, Sell, Hold).}",calculation_knowledge,"[17, 26]"
1069,crypto,40,Critically Over-Leveraged Position,Identifies positions with extremely dangerous leverage levels requiring immediate risk management.,"A position that qualifies as an Over-Leveraged Position where additionally the Effective Leverage exceeds 20 and the Margin Utilization exceeds 90%, creating extreme liquidation risk.",domain_knowledge,"[13, 33, 18]"
1070,crypto,41,High-Quality Arbitrage Opportunity,Identifies particularly favorable arbitrage opportunities with minimal execution risk.,"An Arbitrage Window where the Arbitrage ROI exceeds 0.5% and the Market Efficiency Ratio is less than 1.2, indicating high potential return with low execution risk.",domain_knowledge,"[12, 35, 9]"
1071,crypto,42,Technical Reversal Signal,Identifies strong indications of potential market direction reversal.,"A market condition where Technical Signal Strength exceeds 8 in absolute value while simultaneously showing Momentum Divergence, providing reinforcing signals of a potential trend reversal.",domain_knowledge,"[17, 39]"
1072,crypto,43,Liquidity Constrained Position,Identifies positions that may be difficult to exit due to insufficient market liquidity.,"A position where the Market Depth Ratio is less than 2.0, indicating that the position size is large relative to available market depth, potentially leading to significant slippage upon exit.",domain_knowledge,[36]
1073,crypto,44,Optimal Trading Window,Identifies periods with ideal conditions for order execution.,"Market conditions where the Volatility-Adjusted Spread is less than 1.0 and the Market Maker Activity indicates 'High', suggesting tight spreads relative to volatility and strong liquidity provision.",domain_knowledge,"[14, 37]"
1074,crypto,45,Risk-Efficient Position,Identifies positions with favorable risk-adjusted characteristics.,"A position where the Risk-Adjusted Return exceeds 1.5 and the Risk-to-Reward Ratio is less than 0.5, indicating strong returns relative to risk exposure and favorable potential profit compared to potential loss.",domain_knowledge,"[30, 38]"
1075,crypto,46,Whale-Driven Market,Identifies periods where large traders significantly influence price direction.,"Market conditions where whalemotion is 'High' and there is at least one Whale Order in the same direction as the Smart Money Flow, indicating coordinated activity among large market participants.",domain_knowledge,"[10, 15, 27]"
1076,crypto,47,Liquidation Cascade Risk,Identifies market conditions prone to cascading liquidations.,"Market conditions where more than 15% of open positions are classified as Liquidation Risk Level 'High Risk' and the Order Book Imbalance Ratio exceeds 0.3 in absolute value, indicating concentrated risk and imbalanced liquidity.",domain_knowledge,"[11, 32]"
1077,crypto,48,Perfect Technical Setup,Identifies ideal conditions for technical trading strategies.,"Market conditions where Technical Signal Strength exceeds 7, the techmeter direction matches mktfeel sentiment direction, and no Momentum Divergence is present, indicating strong, consistent technical signals.",domain_knowledge,"[17, 25, 26, 39]"
1078,crypto,49,Flash Crash Vulnerability,"Identifies conditions where markets are susceptible to sudden, severe price drops.","Market conditions where Liquidation Cascade Risk is present, more than 30% of positions qualify as Over-Leveraged Position, and a Liquidity Crisis is developing, creating perfect conditions for a potential flash crash.",domain_knowledge,"[13, 16, 47]"
1079,crypto,50,Flow Dominance,"Categorizes market flow based on which group (smart money, retail, or institutional) has the highest trading volume.",Categorized as 'Smart Money Dominant' when smartforce > retail_flow * 1.2 AND smartforce > inst_flow * 1.2; 'Retail Dominant' when retail_flow > smartforce * 1.2 AND retail_flow > inst_flow * 1.2; 'Institutional Dominant' when inst_flow > smartforce * 1.2 AND inst_flow > retail_flow * 1.2; otherwise 'Mixed'.,domain_knowledge,-1
1080,crypto,51,Smart Money Accuracy,Measures the success rate of smart money flow in predicting the 4-hour price movement direction.,"The proportion of times the Smart Money Flow direction matches the 4-hour price movement direction, calculated as: $$ \frac{\text{COUNT(CASE WHEN (smartforce > retailflow AND smartforce > instflow AND next\_price\_4h > mid\_price) OR (smartforce < retailflow AND smartforce < instflow AND next\_price\_4h < mid\_price) THEN 1 ELSE 0 END)}}{\text{COUNT(*)}} $$",calculation_knowledge,[15]
1081,crypto,52,Effective Leverage Risk Classification,Categorizes positions based on their effective leverage to determine risk exposure.,"A position is labeled as 'High Risk' if its Effective Leverage exceeds 20, otherwise as 'Normal'.",domain_knowledge,[33]