schema_version: "0.1" created: "2026-05-16" source_inventory: observables/observables.yaml status: sparse_first_algorithm_rule_spec purpose: > Sparse support, counterevidence, and explanation rules for cases where only a small number of non-core raw features are available. These rules can route review, modestly adjust confidence, or explain candidate activity, but they do not create a positive training label by themselves. scope_policy: trusted_local_inputs: "Only raw feature IDs from observables/observables.yaml are used as inputs." relationship_to_aggregation_rules: > This file emits weak support, counterevidence, and explanation screens for the public aggregation layer. Support screens route, strengthen, or explain candidates but do not create training identity by themselves. modest_effect_policy: > Single-feature support has score_effect or routing_effect capped to weak or review-only. Pair rules can have larger but still non-primary effects when time overlap and coverage are established. support_categories: - id: physical_activity_support name: Power, energy, thermal, and cooling support algorithm_treatment: "Use as physical consistency support or physical anomaly routing, not workload identity." rule_ids: - physical_power_load_support_screen - cooling_thermal_response_support_screen - physical_activity_pair_support_screen - id: large_model_context_support name: Large-model memory context algorithm_treatment: "Use to strengthen large-model context only after core activity exists." rule_ids: - memory_residency_context_screen - memory_capacity_residency_pair_screen - id: data_lifecycle_support name: Data staging, cache, and copy lifecycle algorithm_treatment: "Use as weak dataset or artifact movement context; check explicit storage-operation explanations first." rule_ids: - storage_staging_support_screen - object_copy_data_movement_support_screen - cache_epoch_context_screen - id: topology_context_support name: Topology and distributed-work context algorithm_treatment: "Use as distributed-work plausibility context, not fabric activity." rule_ids: - topology_distributed_work_support_screen - id: serving_counterevidence name: Serving-like counterevidence algorithm_treatment: "Use to weaken non-serving or training interpretations when aligned with activity." rule_ids: - serving_like_network_counterevidence_screen - id: storage_operation_explanation name: Storage operation explanations algorithm_treatment: "Use to explain storage, WAN, or internal-flow bursts before treating them as checkpoint or collective evidence." rule_ids: - storage_operation_explanation_screen - id: benchmark_hpc_alternative name: Benchmark and HPC alternatives algorithm_treatment: "Use to route fabric-heavy candidates to benchmark/HPC review before promotion." rule_ids: - benchmark_like_alternative_screen - hpc_mpi_alternative_screen - id: lifecycle_operational_context name: Lifecycle, preemption, and maintenance context algorithm_treatment: "Use to explain starts, restarts, gaps, fragmented activity, and checkpoint bursts." rule_ids: - lifecycle_restart_or_preemption_context_screen rule_category_membership: membership_policy: > Categories are evidentiary roles. Each sparse support rule has exactly one primary category for default routing and may list additional categories for scoring or suppressor checks. rules: - rule_id: physical_power_load_support_screen primary_category: physical_activity_support additional_categories: [] - rule_id: cooling_thermal_response_support_screen primary_category: physical_activity_support additional_categories: [] - rule_id: physical_activity_pair_support_screen primary_category: physical_activity_support additional_categories: [] - rule_id: memory_residency_context_screen primary_category: large_model_context_support additional_categories: [] - rule_id: memory_capacity_residency_pair_screen primary_category: large_model_context_support additional_categories: [] - rule_id: storage_staging_support_screen primary_category: data_lifecycle_support additional_categories: [] - rule_id: object_copy_data_movement_support_screen primary_category: data_lifecycle_support additional_categories: [storage_operation_explanation] - rule_id: cache_epoch_context_screen primary_category: data_lifecycle_support additional_categories: [] - rule_id: topology_distributed_work_support_screen primary_category: topology_context_support additional_categories: [] - rule_id: serving_like_network_counterevidence_screen primary_category: serving_counterevidence additional_categories: [] - rule_id: storage_operation_explanation_screen primary_category: storage_operation_explanation additional_categories: [data_lifecycle_support] - rule_id: benchmark_like_alternative_screen primary_category: benchmark_hpc_alternative additional_categories: [] - rule_id: hpc_mpi_alternative_screen primary_category: benchmark_hpc_alternative additional_categories: [topology_context_support] - rule_id: lifecycle_restart_or_preemption_context_screen primary_category: lifecycle_operational_context additional_categories: [storage_operation_explanation] training_support_rules: - id: physical_power_load_support_screen purpose: sparse_physical_activity_support raw_feature_budget: 1 required_feature_sets: any_one_of: - [accelerator_device_power_energy] - [server_system_input_power] - [rack_pdu_it_power] - [facility_or_room_it_power_rollup] emitted_statement: label: physical_activity_support_screen category: physical_activity_support max_confidence: screen statement: "Power or energy telemetry shows material physical load in the monitored scope; workload identity is unresolved." trigger: type: one_feature_physical_load_logic evaluation_window: physical_power_sample_window conditions: all: - metric: baseline_subtracted_physical_load_score op: ">=" value_ref: min_sparse_physical_load_score - metric: physical_power_coverage_fraction op: ">=" value_ref: min_sparse_physical_channel_coverage calibration_defaults: - name: min_sparse_physical_load_score value: 0.50 unit: score_0_to_1 evidence_status: calibration_default source_refs: [S07, S08, S09, S40, S41] rationale: "A moderate normalized, baseline-subtracted physical load is useful sparse support, but it is not workload-specific." counterevidence_or_failure_modes: - "CPU, storage, network, mixed-tenant load, burn-in, or cooling faults can raise power" calibration_note: "Replace with rack/device/facility baselines and known workload power distributions." - name: min_sparse_physical_channel_coverage value: 0.60 unit: fraction evidence_status: source_informed source_refs: [S07, S08, S09, S40, S41] rationale: "Sparse support can emit at lower coverage than strong discrepancy rules but must still report coverage." counterevidence_or_failure_modes: - "BMS/DCIM sampling can be sparse or scope-mismatched" calibration_note: "Replace with source-specific coverage and sampling intervals." score_effect: training_core_confidence_delta: 0 support_score_cap: 0.35 explanation_priority: low routing_effect: route_to: physical_consistency_join missing_joins_to_report: [accelerator_activity, allocation_or_running, cooling_response] false_positive_or_limitations: - "Power alone cannot classify workload type." - "Scope mapping determines whether power belongs to accelerator-bearing assets." cannot_conclude: [training, accelerator_activity, achieved_compute] source_refs: [S07, S08, S09, S40, S41] - id: cooling_thermal_response_support_screen purpose: sparse_thermal_or_cooling_support raw_feature_budget: 1 required_feature_sets: any_one_of: - [accelerator_temperature_celsius] - [inlet_outlet_air_or_coolant_temperature] - [coolant_flow_rate] - [coolant_pressure] - [pump_speed_or_state] - [valve_position_or_state] - [cooling_alarm_state] - [fan_speed_rpm_or_percent] - [airflow_rate] - [thermal_control_mode] - [source_reported_airflow_state] - [outdoor_air_temperature_celsius] - [outdoor_humidity_percent] - [hvac_control_mode] - [economizer_state] - [facility_temperature_setpoint_celsius] emitted_statement: label: thermal_or_cooling_context_screen category: physical_activity_support max_confidence: screen statement: "Thermal or cooling telemetry provides physical context for load, cooling response, or alarms; it does not identify training." trigger: type: one_feature_cooling_thermal_logic evaluation_window: thermal_or_cooling_sample_window conditions: any: - metric: thermal_response_score op: ">=" value_ref: min_thermal_response_score - metric: cooling_alarm_active op: "==" value: true - metric: cooling_control_change_score op: ">=" value_ref: min_cooling_control_change_score calibration_defaults: - name: min_thermal_response_score value: 0.50 unit: score_0_to_1 evidence_status: calibration_default source_refs: [S40, S41] rationale: "A normalized temperature, flow, fan, or cooling response can support physical-load review." counterevidence_or_failure_modes: - "ambient conditions, economizer state, fixed fan policy, or unrelated heat load can drive response" calibration_note: "Calibrate by cooling design, ambient conditions, and telemetry lag." - name: min_cooling_control_change_score value: 0.50 unit: score_0_to_1 evidence_status: calibration_default source_refs: [S40, S41] rationale: "Control changes are weak context until joined to power or activity." counterevidence_or_failure_modes: - "control changes may be scheduled or ambient-driven" calibration_note: "Replace with facility control-mode and setpoint change baselines." score_effect: training_core_confidence_delta: 0 support_score_cap: 0.25 explanation_priority: low routing_effect: route_to: physical_context_review missing_joins_to_report: [power, accelerator_activity, maintenance] false_positive_or_limitations: - "Cooling response can lag or precede IT load depending on control mode." cannot_conclude: [training, accelerator_activity] source_refs: [S40, S41] - id: physical_activity_pair_support_screen purpose: sparse_physical_activity_pair_support raw_feature_budget: 2 required_feature_sets: any_one_of: - [accelerator_device_power_energy, tensor_matrix_mxu_neuron_or_engine_active_fraction] - [accelerator_device_power_energy, accelerator_busy_or_utilization_fraction] - [rack_pdu_it_power, tensor_matrix_mxu_neuron_or_engine_active_fraction] - [rack_pdu_it_power, accelerator_busy_or_utilization_fraction] - [server_system_input_power, tensor_matrix_mxu_neuron_or_engine_active_fraction] - [facility_or_room_it_power_rollup, tensor_matrix_mxu_neuron_or_engine_active_fraction] emitted_statement: label: physical_activity_support_screen category: physical_activity_support max_confidence: weak statement: "Power or energy load overlaps accelerator activity, supporting that substantial accelerator-bearing infrastructure work occurred." trigger: type: two_feature_physical_activity_overlap_logic evaluation_window: lag_adjusted_power_activity_window conditions: all: - metric: baseline_subtracted_physical_load_score op: ">=" value_ref: min_pair_physical_load_score - metric: sparse_activity_score op: ">=" value_ref: min_pair_activity_score - metric: lag_adjusted_power_activity_overlap_fraction op: ">=" value_ref: min_pair_power_activity_overlap calibration_defaults: - name: min_pair_physical_load_score value: 0.55 unit: score_0_to_1 evidence_status: calibration_default source_refs: [S07, S08, S09, S40, S41] rationale: "Pair support should require slightly stronger physical load than a one-feature screen." counterevidence_or_failure_modes: - "mixed-tenant and non-accelerator loads can still overlap activity" calibration_note: "Calibrate by mapped rack/device power baselines." - name: min_pair_activity_score value: 0.50 unit: score_0_to_1 evidence_status: calibration_default source_refs: [S07, S08, S36, S37, S38, S39] rationale: "Physical support should overlap material accelerator activity." counterevidence_or_failure_modes: - "serving, inference, HPC, benchmarks, and burn-in can pass" calibration_note: "Replace with local activity-score distributions." - name: min_pair_power_activity_overlap value: 0.50 unit: fraction evidence_status: calibration_default source_refs: [S07, S08, S09, S40, S41] rationale: "Thermal and power signals can lag activity; half-overlap is an executable sparse default." counterevidence_or_failure_modes: - "coarse timestamps can create false overlap" calibration_note: "Tune lag and overlap by source sampling and cooling design." score_effect: training_core_confidence_delta: 0 support_score_cap: 0.45 explanation_priority: low routing_effect: route_to: join_with_identity_shape missing_joins_to_report: [fabric_identity_shape, checkpoint_identity_shape, serving_counterevidence] false_positive_or_limitations: - "Aligned physical load and activity still do not distinguish training from other accelerator work." cannot_conclude: [training, model_development] source_refs: [S07, S08, S09, S36, S37, S38, S39, S40, S41] - id: memory_residency_context_screen purpose: sparse_large_model_memory_context raw_feature_budget: 1 required_feature_sets: any_one_of: - [hbm_or_device_memory_used] emitted_statement: label: large_model_memory_context_screen category: large_model_context_support max_confidence: screen statement: "High device-memory use is large-model context, but can also be serving, cache, allocation, or idle residency." trigger: type: one_feature_memory_residency_logic evaluation_window: memory_used_sample_window conditions: all: - metric: hbm_or_device_memory_used_bytes op: ">=" value_ref: min_sparse_memory_used_bytes - metric: memory_value_type op: in value_ref: supportive_memory_value_types calibration_defaults: - name: min_sparse_memory_used_bytes value: 40000000000 unit: bytes evidence_status: calibration_default source_refs: [S04, S05, S06, S07, S08, S09] rationale: "A 40 GB sparse screen catches materially large accelerator memory residency while remaining below current high-end capacity." counterevidence_or_failure_modes: - "large inference, embeddings, caching, and idle loaded models can pass" - "adapter or sharded training can be below this threshold" calibration_note: "Replace with local memory-use distributions by workload and accelerator." - name: supportive_memory_value_types value: [used, allocated, high_water, resident] unit: categorical_set evidence_status: source_informed source_refs: [S07, S08, S09] rationale: "Used, allocated, resident, or high-water values can support memory-residency context." counterevidence_or_failure_modes: - "free-memory values require inversion and capacity context" calibration_note: "Normalize telemetry source memory value semantics." score_effect: training_core_confidence_delta: 0 support_score_cap: 0.30 explanation_priority: low routing_effect: route_to: large_model_context_join missing_joins_to_report: [memory_capacity_bytes, accelerator_activity, serving_counterevidence] false_positive_or_limitations: - "Memory residency alone is not training evidence." cannot_conclude: [training, activity, model_size] source_refs: [S04, S05, S06, S07, S08, S09] - id: memory_capacity_residency_pair_screen purpose: sparse_large_model_memory_pair_support raw_feature_budget: 2 required_feature_sets: any_one_of: - [hbm_or_device_memory_used, memory_capacity_bytes] emitted_statement: label: large_model_memory_context_screen category: large_model_context_support max_confidence: weak statement: "Device memory residency occupies a material fraction of memory capacity, supporting large-model context if joined with activity evidence." trigger: type: two_feature_memory_fraction_logic evaluation_window: memory_used_capacity_overlap_window conditions: all: - metric: memory_residency_fraction op: ">=" value_ref: min_sparse_memory_residency_fraction - metric: memory_capacity_overlap_fraction op: ">=" value_ref: min_memory_capacity_overlap_fraction calibration_defaults: - name: min_sparse_memory_residency_fraction value: 0.60 unit: fraction_of_device_memory evidence_status: calibration_default source_refs: [S04, S05, S06, S07, S08, S09] rationale: "High fractional memory residency is stronger context than raw bytes, but remains workload-ambiguous." counterevidence_or_failure_modes: - "serving and cache-heavy workloads can occupy most memory" - "ZeRO, offload, and adapter training can use less memory" calibration_note: "Calibrate by model family, sharding, serving, and cache behavior." - name: min_memory_capacity_overlap_fraction value: 0.80 unit: fraction evidence_status: source_informed source_refs: [S07, S08, S09] rationale: "The capacity and use samples should cover the same memory window." counterevidence_or_failure_modes: - "sample timing mismatch can distort fractions" calibration_note: "Tune by scrape period and device mapping." score_effect: training_core_confidence_delta: 0 support_score_cap: 0.45 explanation_priority: low routing_effect: route_to: join_with_activity_and_checkpoint missing_joins_to_report: [accelerator_activity, checkpoint_identity_shape, serving_counterevidence] false_positive_or_limitations: - "Fractional residency is not a workload identity signal by itself." cannot_conclude: [training, activity] source_refs: [S04, S05, S06, S07, S08, S09] - id: storage_staging_support_screen purpose: sparse_data_staging_support raw_feature_budget: 1 required_feature_sets: any_one_of: - [storage_read_operation_bytes] - [object_storage_operation_counts] - [north_south_external_egress] - [data_center_interconnect_wan_usage] emitted_statement: label: data_staging_support_screen category: data_lifecycle_support max_confidence: screen statement: "Large or repeated read, object, egress, or DCI movement can be dataset or artifact staging context, but many non-training workflows share it." trigger: type: one_feature_storage_staging_logic evaluation_window: storage_or_egress_interval conditions: all: - metric: normalized_staging_or_transfer_score op: ">=" value_ref: min_sparse_staging_score - metric: storage_or_network_coverage_fraction op: ">=" value_ref: min_sparse_staging_coverage_fraction calibration_defaults: - name: min_sparse_staging_score value: 0.50 unit: score_0_to_1 evidence_status: calibration_default source_refs: [S28, S29, S31, S32, S33, S34, S35] rationale: "Use normalized volume relative to local baseline or participant memory rather than raw bytes." counterevidence_or_failure_modes: - "backup, restore, ETL, replication, logs, and artifact export can pass" calibration_note: "Replace with storage and network baselines by service, tenant, and workload class." - name: min_sparse_staging_coverage_fraction value: 0.60 unit: fraction evidence_status: source_informed source_refs: [S28, S29, S31, S34] rationale: "Sparse staging support should be retained with moderate coverage but carry explicit caveats." counterevidence_or_failure_modes: - "flow and object logs can be sampled, delayed, best-effort, or skipped" calibration_note: "Replace with per-source logging completeness." score_effect: training_core_confidence_delta: 0 support_score_cap: 0.35 explanation_priority: medium routing_effect: route_to: data_lifecycle_join missing_joins_to_report: [compute_window, storage_operation_intervals, accelerator_activity] false_positive_or_limitations: - "Staging support only strengthens training after core activity exists." cannot_conclude: [training, checkpointing, model_development] source_refs: [S28, S29, S31, S32, S33, S34, S35] - id: object_copy_data_movement_support_screen purpose: sparse_copy_movement_context raw_feature_budget: 1 required_feature_sets: any_one_of: - [object_or_file_copy_movement_records] emitted_statement: label: data_copy_movement_context_screen category: data_lifecycle_support max_confidence: screen statement: "Object or file copy/movement records provide data lifecycle context and may also explain storage or network bursts." trigger: type: one_feature_copy_movement_logic evaluation_window: copy_movement_interval conditions: all: - metric: bytes_moved op: ">=" value_ref: min_copy_bytes_to_emit - metric: route_class_known op: "==" value: true calibration_defaults: - name: min_copy_bytes_to_emit value: 0 unit: bytes evidence_status: source_informed source_refs: [S31, S32, S33, S34, S35] rationale: "Even zero or unknown bytes with a source-emitted copy interval can route explanation review; byte materiality is scored separately." counterevidence_or_failure_modes: - "metadata-only or failed copy events may not explain traffic" calibration_note: "Normalize success status, bytes moved, route class, and source coverage." score_effect: training_core_confidence_delta: 0 support_score_cap: 0.25 explanation_priority: medium routing_effect: route_to: storage_or_network_explanation_join missing_joins_to_report: [storage_operation_intervals, flow_or_fabric_bytes, compute_window] false_positive_or_limitations: - "Copy movement can be dataset staging, backup, migration, artifact export, or ETL." cannot_conclude: [training, checkpointing] source_refs: [S31, S32, S33, S34, S35] - id: cache_epoch_context_screen purpose: sparse_cache_epoch_context raw_feature_budget: 1 required_feature_sets: any_one_of: - [cache_hit_miss_counters] emitted_statement: label: cache_epoch_context_screen category: data_lifecycle_support max_confidence: screen statement: "Cache hit/miss curves provide weak data-reuse or input-bottleneck context; they are highly workload and tenant dependent." trigger: type: one_feature_cache_context_logic evaluation_window: cache_counter_interval conditions: any: - metric: cache_miss_burst_score op: ">=" value_ref: min_cache_miss_burst_score - metric: cache_hit_rate_change_score op: ">=" value_ref: min_cache_hit_rate_change_score calibration_defaults: - name: min_cache_miss_burst_score value: 0.50 unit: score_0_to_1 evidence_status: calibration_default source_refs: [S33, S34, S35] rationale: "Cache miss bursts can route data-loading review but are not portable across storage and cache systems." counterevidence_or_failure_modes: - "serving cold starts, ETL, package installs, and multi-tenant cache churn can pass" calibration_note: "Replace with cache baselines and known dataset-pass traces." - name: min_cache_hit_rate_change_score value: 0.50 unit: score_0_to_1 evidence_status: calibration_default source_refs: [S33, S34, S35] rationale: "Hit-rate changes can support epoch-like reuse only after joining with compute windows." counterevidence_or_failure_modes: - "cache warming and serving can produce similar curves" calibration_note: "Calibrate by cache policy and workload class." score_effect: training_core_confidence_delta: 0 support_score_cap: 0.20 explanation_priority: low routing_effect: route_to: data_reuse_review missing_joins_to_report: [storage_reads, accelerator_activity, compute_window] false_positive_or_limitations: - "Cache signals are weak without compute and storage context." cannot_conclude: [training, epoch_count] source_refs: [S33, S34, S35] - id: topology_distributed_work_support_screen purpose: sparse_topology_context_support raw_feature_budget: 1 required_feature_sets: any_one_of: - [scaleout_fabric_domain_graph] - [local_accelerator_interconnect_domain] - [cross_site_or_dci_connectivity] emitted_statement: label: topology_distributed_work_support_screen category: topology_context_support max_confidence: screen statement: "Topology capability supports distributed-work plausibility, but observed activity and identity-shape evidence are still required." trigger: type: one_feature_topology_context_logic evaluation_window: topology_validity_or_sample_window conditions: all: - metric: topology_context_score op: ">=" value_ref: min_sparse_topology_context_score - metric: topology_coverage_fraction op: ">=" value_ref: min_sparse_topology_coverage_fraction calibration_defaults: - name: min_sparse_topology_context_score value: 0.50 unit: score_0_to_1 evidence_status: calibration_default source_refs: [S11, S15, S16, S17, S25, S42, S43] rationale: "A moderate topology score routes distributed-work review without claiming activity." counterevidence_or_failure_modes: - "HPC, benchmark, inference, and storage clusters can have similar topology" calibration_note: "Replace with local topology scores by workload and fabric generation." - name: min_sparse_topology_coverage_fraction value: 0.70 unit: fraction evidence_status: source_informed source_refs: [S25, S42, S43] rationale: "Topology capability must cover most of the sparse context window." counterevidence_or_failure_modes: - "route changes or link failures can invalidate topology capability" calibration_note: "Calibrate by topology inventory update cadence." score_effect: training_core_confidence_delta: 0 support_score_cap: 0.35 explanation_priority: medium routing_effect: route_to: fabric_activity_join missing_joins_to_report: [scaleout_port_tx_rx_bytes_packets, accelerator_activity, allocation_or_running] false_positive_or_limitations: - "Topology capability alone is not fabric telemetry." cannot_conclude: [training, collective_activity] source_refs: [S11, S15, S16, S17, S25, S42, S43] - id: serving_like_network_counterevidence_screen purpose: sparse_serving_counterevidence raw_feature_budget: 1 required_feature_sets: any_one_of: - [load_balancer_gateway_flow_activity] - [north_south_external_egress] - [vpc_or_nsg_flow_records] emitted_statement: label: serving_like_counterevidence_screen category: serving_counterevidence max_confidence: screen statement: "Serving-like gateway, external egress, or flow shape is counterevidence for non-serving training interpretations when aligned with accelerator activity." trigger: type: one_feature_serving_counterevidence_logic evaluation_window: serving_network_interval conditions: all: - metric: serving_counterevidence_score op: ">=" value_ref: min_sparse_serving_counterevidence_score - metric: serving_channel_coverage_fraction op: ">=" value_ref: min_sparse_serving_coverage_fraction calibration_defaults: - name: min_sparse_serving_counterevidence_score value: 0.65 unit: score_0_to_1 evidence_status: calibration_default source_refs: [S27, S28, S29, S30, S34] rationale: "Serving-like network shape should be retained as counterevidence, but one channel should not fully suppress a candidate alone." counterevidence_or_failure_modes: - "dataset fetches, artifact export, batch APIs, service mesh, and logging can resemble serving" calibration_note: "Replace with local serving, batch inference, export, and training network traces." - name: min_sparse_serving_coverage_fraction value: 0.60 unit: fraction evidence_status: source_informed source_refs: [S27, S28, S29, S30, S34] rationale: "Presence of serving-like records can be useful at moderate coverage; absence still cannot imply non-serving." counterevidence_or_failure_modes: - "flow logs can be sampled, aggregated, skipped, delayed, or disabled" calibration_note: "Replace with source-specific flow/gateway coverage." score_effect: training_core_confidence_delta: 0 support_score_cap: 0.40 explanation_priority: high suppresses_confidence_at_or_above: medium_only_when_joined_to_activity routing_effect: route_to: serving_false_positive_review missing_joins_to_report: [accelerator_activity, storage_operation_intervals, compute_window] false_positive_or_limitations: - "Serving counterevidence should be time-aligned with activity before weakening a training candidate." cannot_conclude: [not_training, serving_workload] source_refs: [S27, S28, S29, S30, S34] - id: storage_operation_explanation_screen purpose: sparse_storage_operation_explanation raw_feature_budget: 1 required_feature_sets: any_one_of: - [storage_operation_intervals] emitted_statement: label: storage_operation_explanation_screen category: storage_operation_explanation max_confidence: screen statement: "Explicit storage backup, restore, rebuild, replication, migration, copy, or ETL intervals can explain storage, WAN, or fabric-like traffic." trigger: type: one_feature_storage_operation_explanation_logic evaluation_window: storage_operation_interval conditions: all: - metric: operation_type op: in value_ref: explanatory_storage_operation_types - metric: bytes_moved op: ">=" value_ref: min_storage_operation_bytes_to_emit calibration_defaults: - name: explanatory_storage_operation_types value: [backup, restore, rebuild, replication, migration, disaster_recovery, snapshot, object_copy, etl_export] unit: categorical_set evidence_status: source_informed source_refs: [S31, S32, S33, S34, S35] rationale: "Source-emitted storage operation labels are direct explanation candidates for storage and network movement." counterevidence_or_failure_modes: - "operation labels can be coarse and overlap real training" calibration_note: "Normalize local storage operation taxonomy." - name: min_storage_operation_bytes_to_emit value: 0 unit: bytes evidence_status: source_informed source_refs: [S31, S32, S33, S34, S35] rationale: "Even operation intervals without byte fields can route explanation checks; byte materiality is handled when traffic features are present." counterevidence_or_failure_modes: - "zero-byte or failed operations may not explain traffic" calibration_note: "Use source status, bytes moved, and object count where available." score_effect: training_core_confidence_delta: 0 support_score_cap: 0.35 explanation_priority: high suppresses_confidence_at_or_above: medium_only_when_overlapping_activity routing_effect: route_to: storage_explanation_join missing_joins_to_report: [storage_write_operation_bytes, scaleout_port_tx_rx_bytes_packets, vpc_or_nsg_flow_records] false_positive_or_limitations: - "Explicit storage operations explain only overlapping and attributable bytes." cannot_conclude: [not_training, no_checkpointing] source_refs: [S31, S32, S33, S34, S35] - id: benchmark_like_alternative_screen purpose: sparse_benchmark_like_counterevidence raw_feature_budget: 1 required_feature_sets: any_one_of: - [scaleout_port_tx_rx_bytes_packets] - [local_interconnect_tx_rx_bytes] - [fabric_port_device_sample_counters] emitted_statement: label: benchmark_like_alternative_screen category: benchmark_hpc_alternative max_confidence: screen statement: "Highly regular fabric bursts can be benchmark-like or burn-in-like and should suppress overpromotion until lifecycle and checkpoint evidence are checked." trigger: type: one_feature_benchmark_like_fabric_logic evaluation_window: fabric_burst_interval conditions: all: - metric: fabric_burst_regularity_score op: ">=" value_ref: min_sparse_benchmark_regularity_score - metric: fabric_burst_duration_seconds op: "<=" value_ref: max_sparse_benchmark_duration_seconds calibration_defaults: - name: min_sparse_benchmark_regularity_score value: 0.85 unit: score_0_to_1 evidence_status: calibration_default source_refs: [S11, S15, S16, S17, S25, S42, S43] rationale: "Synthetic communication tests are often very regular; exact threshold is not portable." counterevidence_or_failure_modes: - "some training steps are regular and some benchmarks are long or irregular" calibration_note: "Calibrate with local NCCL/MPI benchmark, burn-in, and training traces." - name: max_sparse_benchmark_duration_seconds value: 7200 unit: seconds evidence_status: calibration_default source_refs: [S11, S15, S16, S17, S25] rationale: "Short high-regularity fabric windows are useful benchmark-like screens." counterevidence_or_failure_modes: - "short fine-tunes and long benchmarks exist" calibration_note: "Replace with local benchmark and job-duration distributions." score_effect: training_core_confidence_delta: 0 support_score_cap: 0.40 explanation_priority: high suppresses_confidence_at_or_above: medium_only_when_joined_to_candidate routing_effect: route_to: benchmark_or_burnin_review missing_joins_to_report: [accelerator_activity, checkpoint_identity_shape, allocation_or_running] false_positive_or_limitations: - "Fabric regularity alone cannot identify benchmark or training." cannot_conclude: [not_training, benchmark] source_refs: [S11, S15, S16, S17, S25, S42, S43] - id: hpc_mpi_alternative_screen purpose: sparse_hpc_mpi_alternative raw_feature_budget: 2 required_feature_sets: any_one_of: - [scaleout_port_tx_rx_bytes_packets, tensor_matrix_mxu_neuron_or_engine_active_fraction] - [scaleout_port_tx_rx_bytes_packets, generic_achieved_operation_rate] - [scaleout_port_tx_rx_bytes_packets, accelerator_busy_or_utilization_fraction] - [local_interconnect_tx_rx_bytes, tensor_matrix_mxu_neuron_or_engine_active_fraction] - [fabric_port_device_sample_counters, generic_achieved_operation_rate] emitted_statement: label: hpc_mpi_alternative_screen category: benchmark_hpc_alternative max_confidence: weak statement: "Fabric cadence and accelerator activity can also match HPC or MPI-style workloads; checkpoint or model-lifecycle support is needed before training promotion." trigger: type: two_feature_fabric_activity_hpc_alternative_logic evaluation_window: fabric_activity_overlap_window conditions: all: - metric: sparse_fabric_cadence_score op: ">=" value_ref: min_sparse_hpc_fabric_cadence_score - metric: sparse_activity_score op: ">=" value_ref: min_sparse_hpc_activity_score - metric: fabric_activity_overlap_fraction op: ">=" value_ref: min_sparse_hpc_overlap_fraction calibration_defaults: - name: min_sparse_hpc_fabric_cadence_score value: 0.60 unit: score_0_to_1 evidence_status: mechanism_inferred source_refs: [S11, S15, S16, S17, S25] rationale: "HPC/MPI and distributed training can both produce collective-like fabric cadence." counterevidence_or_failure_modes: - "without domain/runtime context the alternative remains weak" calibration_note: "Calibrate with local HPC, MPI, benchmark, and training traces." - name: min_sparse_hpc_activity_score value: 0.50 unit: score_0_to_1 evidence_status: calibration_default source_refs: [S07, S08, S36, S37, S38, S39] rationale: "HPC alternative should overlap material accelerator activity." counterevidence_or_failure_modes: - "communication-only tests can have lower accelerator activity" calibration_note: "Tune by workload class and accelerator family." - name: min_sparse_hpc_overlap_fraction value: 0.50 unit: fraction evidence_status: calibration_default source_refs: [S11, S15, S16, S17] rationale: "Fabric and compute should overlap materially before routing to HPC/MPI alternative review." counterevidence_or_failure_modes: - "coarse telemetry can create false overlap" calibration_note: "Replace with source-specific clock alignment and aggregation behavior." score_effect: training_core_confidence_delta: 0 support_score_cap: 0.45 explanation_priority: medium suppresses_confidence_at_or_above: medium_only_when_checkpoint_support_missing routing_effect: route_to: hpc_or_mpi_review missing_joins_to_report: [checkpoint_identity_shape, serving_counterevidence, domain_runtime_context] false_positive_or_limitations: - "HPC/MPI alternative is a routing screen, not a definitive label." cannot_conclude: [not_training, hpc] source_refs: [S07, S08, S11, S15, S16, S17, S25, S36, S37, S38, S39] - id: lifecycle_restart_or_preemption_context_screen purpose: sparse_lifecycle_operational_context raw_feature_budget: 1 required_feature_sets: any_one_of: - [resource_container_lifecycle_events] - [allocation_preemption_events] - [maintenance_operation_intervals] emitted_statement: label: lifecycle_restart_or_preemption_context_screen category: lifecycle_operational_context max_confidence: screen statement: "Lifecycle, preemption, or maintenance events can explain starts, restarts, fragmented activity, checkpoint bursts, or telemetry gaps." trigger: type: one_feature_lifecycle_context_logic evaluation_window: lifecycle_or_preemption_event_window conditions: any: - metric: lifecycle_event_count op: ">=" value_ref: min_lifecycle_event_count_to_emit - metric: preempted_resource_count op: ">=" value_ref: min_preempted_resource_count_to_emit - metric: affected_asset_count op: ">=" value_ref: min_lifecycle_maintenance_affected_assets calibration_defaults: - name: min_lifecycle_event_count_to_emit value: 1 unit: events evidence_status: source_backed source_refs: [S21, S22, S38] rationale: "Any source-emitted lifecycle event can bound candidate windows or explain fragmented activity." counterevidence_or_failure_modes: - "generic lifecycle events may be unrelated to accelerator work" calibration_note: "Normalize event reasons and container-to-resource mapping." - name: min_preempted_resource_count_to_emit value: 1 unit: resources evidence_status: source_informed source_refs: [S18, S21, S22] rationale: "Preemption of any accelerator-relevant resource can explain restarts or checkpoint bursts." counterevidence_or_failure_modes: - "preemption records can refer to CPU or non-accelerator resources" calibration_note: "Map resource_type to accelerator and workload scope." - name: min_lifecycle_maintenance_affected_assets value: 1 unit: assets evidence_status: source_informed source_refs: [S18, S40, S41] rationale: "Maintenance affecting one mapped asset can explain gaps or restarts in sparse telemetry." counterevidence_or_failure_modes: - "broad maintenance intervals can over-explain unrelated events" calibration_note: "Join maintenance asset categories to cluster, storage, power, and fabric scopes." score_effect: training_core_confidence_delta: 0 support_score_cap: 0.30 explanation_priority: medium routing_effect: route_to: lifecycle_explanation_join missing_joins_to_report: [compute_window, checkpoint_write_window, telemetry_coverage_fraction] false_positive_or_limitations: - "Lifecycle context explains timing but does not identify workload type." cannot_conclude: [training, no_training] source_refs: [S18, S21, S22, S38, S40, S41]