task stringclasses 4
values | question stringclasses 5
values | label stringclasses 11
values | cot stringlengths 2.94k 8.35k | step1_label stringclasses 1
value | step2_label stringlengths 41 227 | step4_label stringclasses 51
values | step6_label stringclasses 23
values | timeseries2 stringlengths 4.13k 6.47k |
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ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | other cardiac rhythms | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying a single-lead ECG time series into one of four predefined rhythm categories (normal sinus rhythm, atrial fibrillation, other cardiac rhythms, or noise). The core objective is to ana... | This is a classification task. | P wave presence/absence; qrs complex regularity; r-r interval variability; signal-to-noise ratio; baseline stability; trend; amplitude; fluctuation; continuity; judgment criteria; threshold values. | B) atrial fibrillation | C) other cardiac rhythms | 1.1810, 1.1230, 1.0520, 1.0070, 1.0540, 1.2050, 1.3130, 1.2980, 1.0790, 0.7340, 0.4070, 0.2020, 0.1040, 0.0470, -0.0040, -0.0850, -0.1450, -0.1710, -0.1600, -0.1010, -0.0320, 0.0220, 0.0720, 0.1070, 0.1390, 0.1770, 0.1830, 0.1790, 0.1730, 0.1440, 0.0970, 0.0770, 0.0520, 0.0190, 0.0100, 0.0010, 0.0080, 0.0140, 0.0230, 0... |
CTU | You are a time series analysis expert. This is a time series signal derived from electricity usage patterns in UK households, recorded as part of the government-sponsored study "Powering the Nation": <ts><ts/>. The signal represents energy consumption sampled every 2 minutes over a 24-hour period, resulting in a series... | Laptop | <THINK>**Step 1. Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires labeling the full time series signal into one of two predefined classes ("Desktop" or "Laptop") based on electricity consumption patterns. Specific keywords like "classify the household’s device us... | This is a classification task. | Baseline consumption level; duration of high-consumption states; frequency of low-power states; fluctuation amplitude; trend; continuity; threshold values. | B)laptop | B)laptop | -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.2859, -0.28... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | normal sinus rhythm | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem explicitly requires classifying the ECG rhythm into one of four predefined categories ("normal sinus rhythm," "atrial fibrillation," "other cardiac rhythms," or "noise"). Keywords like "classify the hea... | This is a classification task. | P wave presence/absence; qrs complex regularity; rr interval variability; signal noise level; trend; amplitude; fluctuation; continuity | A) normal sinus rhythm | A) normal sinus rhythm | -0.0070, -0.0120, -0.0170, -0.0230, -0.0320, -0.0460, -0.0560, -0.0660, -0.0780, -0.0910, -0.1070, -0.1220, -0.1360, -0.1450, -0.1470, -0.1480, -0.1500, -0.1520, -0.1530, -0.1390, -0.1140, -0.0770, -0.0490, -0.0250, -0.0130, -0.0080, -0.0060, -0.0060, -0.0050, -0.0040, -0.0030, -0.0010, 0.0000, 0.0030, 0.0070, 0.0110, ... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | normal sinus rhythm | <THINK>**Step 1. Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem explicitly requires classifying the ECG rhythm into one of four predefined categories ('normal sinus rhythm', 'atrial fibrillation', 'other cardiac rhythms', or 'noise'). The core objective is to analyze w... | This is a classification task. | P wave presence/regularity; qrs complex regularity; rr interval variability; signal-to-noise ratio; trend; amplitude; fluctuation; continuity; judgment criteria; threshold values. | A) normal sinus rhythm. | A) normal sinus rhythm | -0.0490, -0.0470, -0.0410, -0.0290, -0.0470, -0.0660, -0.0800, -0.0820, -0.0750, -0.0660, -0.0540, -0.0640, -0.0790, -0.0850, -0.0890, -0.0940, -0.0990, -0.1020, -0.1070, -0.1110, -0.1140, -0.1150, -0.1160, -0.1170, -0.1200, -0.1250, -0.1280, -0.1280, -0.1230, -0.1130, -0.0980, -0.0850, -0.0710, -0.0560, -0.0380, -0.02... |
RCW | You are analyzing an audio signal to determine the presence of right whale vocalizations.
Up-calls are the most commonly documented right whale vocalisation with anacoustic signature of approximately 60Hz-250Hz, typically lasting 1second.
Right whale calls can often be difficult to hear as the lowfrequency band can b... | No Right Whale | <think>**Step 1 Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires determining whether a given audio signal segment contains a right whale vocalization ("Right Whale Present" or "No Right Whale"). The core objective is binary classification based on predefined acou... | This is a classification task. | Spectral energy concentration in 60hz–250hz band; sustained duration; distinction from low-frequency noise; trend; amplitude; fluctuation; continuity. | B) no right whale. | B) no right whale | 0.0082, -0.0165, -0.0326, -0.0134, -0.0189, -0.0064, -0.0028, 0.0140, -0.0223, -0.0037, -0.0082, 0.0214, -0.0028, 0.0107, -0.0046, 0.0049, 0.0177, -0.0076, 0.0229, -0.0058, 0.0134, 0.0079, -0.0177, 0.0055, 0.0012, -0.0153, -0.0146, 0.0171, -0.0137, -0.0125, 0.0348, 0.0232, 0.0018, -0.0137, -0.0076, -0.0058, 0.0156, 0.0... |
RCW | You are analyzing an audio signal to determine the presence of right whale vocalizations.
Up-calls are the most commonly documented right whale vocalisation with anacoustic signature of approximately 60Hz-250Hz, typically lasting 1second.
Right whale calls can often be difficult to hear as the lowfrequency band can b... | Right Whale Present | <think>Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires determining whether a specific acoustic signal contains a right whale vocalization ("Right Whale Present" or "No Right Whale"). The core objective is to classify the waveform based on predefined c... | This is a classification task. | Frequency band; duration; signal-to-noise ratio; temporal continuity. | A) right whale present | A) right whale present | 0.2441, 0.1447, -0.0272, -0.0922, -0.0995, -0.0619, 0.0732, -0.1215, -0.0824, -0.0330, -0.0134, -0.0617, -0.1010, 0.0970, -0.0415, 0.0427, 0.0263, -0.0012, -0.0128, 0.1031, -0.0076, -0.0888, -0.0351, -0.0403, -0.0360, 0.1132, 0.0781, 0.0555, 0.0693, -0.1386, -0.1587, -0.0238, -0.0537, -0.0311, -0.1703, 0.0769, 0.0363, ... |
CTU | You are a time series analysis expert. This is a time series signal derived from electricity usage patterns in UK households, recorded as part of the government-sponsored study "Powering the Nation": <ts><ts/>. The signal represents energy consumption sampled every 2 minutes over a 24-hour period, resulting in a series... | Desktop | <THINK>Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem explicitly requires classifying a household's device usage pattern into one of two predefined classes ("Desktop" or "Laptop") based on energy consumption time series data. Keywords include "classify the hous... | This is a classification task. | Baseline energy consumption; duration/intensity of energy spikes; stability of energy plateaus; trend; amplitude; fluctuation; continuity. | A)desktop | A)desktop | -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.3350, -0.33... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | noise | <THINK>**Step 1. Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying a single-lead ECG time series into one of four predefined rhythm categories (normal sinus rhythm, atrial fibrillation, other cardiac rhythms, or noise). The core objective is to assign ... | This is a classification task. | P waves; qrs complexes; rhythm regularity; signal noise; trend; amplitude; fluctuation; continuity. | D) noise. | D) noise | -0.1150, -0.0840, -0.0770, -0.0890, -0.0920, -0.0940, -0.0940, -0.0950, -0.0960, -0.0940, -0.0920, -0.0900, -0.0870, -0.0840, -0.0810, -0.0770, -0.0730, -0.0700, -0.0670, -0.0590, -0.0490, -0.0390, -0.0280, -0.0200, -0.0150, -0.0100, -0.0030, 0.0020, 0.0070, 0.0120, 0.0170, 0.0250, 0.0380, 0.0500, 0.0610, 0.0670, 0.072... |
EMG | You are a time series analysis expert. This is a short-duration electromyography (EMG) signal recorded using a concentric needle electrode inserted into the tibialis anterior muscle: <ts><ts/>. The recording captures the amplitude (mV) of electrical activity generated by muscle fibers innervated by motor neurons. This... | Neuropathy | <THINK>**Step 1. Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying an EMG signal into one of three neuromuscular conditions (Healthy, Myopathy, Neuropathy) based on amplitude characteristics. Key phrases include: "corresponds to one of the following n... | This is a classification task. | Amplitude range; amplitude distribution; pathological amplitude thresholds; trend; fluctuation; continuity; judgment criteria; threshold values. | C)neuropathy | C)neuropathy | -0.0200, 0.0067, 0.0100, 0.1033, 0.1083, 0.2100, 0.2133, 0.3367, 0.3417, 0.5117, 0.5783, 0.4500, 0.4333, -0.3350, -0.3683, -0.7367, -0.5433, 0.4367, 0.7433, -1.5983, -2.5817, -1.5767, -1.5100, 0.0800, 0.1067, 0.2433, 0.1667, 0.3817, 0.3850, 0.3317, 0.3267, 0.2450, 0.2417, 0.1833, 0.1800, 0.1167, 0.1167, 0.0933, 0.0950,... |
EMG | You are a time series analysis expert. This is a short-duration electromyography (EMG) signal recorded using a concentric needle electrode inserted into the tibialis anterior muscle: <ts><ts/>. The recording captures the amplitude (mV) of electrical activity generated by muscle fibers innervated by motor neurons. This... | Neuropathy | <THINK>**Step 1. Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires analyzing an EMG signal's amplitude characteristics (specifically approximate min/max values) to classify it into one of three neuromuscular conditions (Healthy, Myopathy, Neuropathy). Keywords lik... | This is a classification task. | Minimum amplitude; maximum amplitude; amplitude range; trend; fluctuation; continuity. | C)neuropathy | C)neuropathy | 0.0050, 0.0033, 0.0067, 0.0083, 0.0050, 0.0083, 0.0033, 0.0033, -0.0033, 0.0000, -0.0133, -0.0133, -0.0200, -0.0217, -0.0150, -0.0150, -0.0100, -0.0100, -0.0067, -0.0050, -0.0133, -0.0117, -0.0217, -0.0217, -0.0317, -0.0300, -0.0333, -0.0333, -0.0400, -0.0400, -0.0467, -0.0467, -0.0417, -0.0433, -0.0400, -0.0400, -0.03... |
EMG | You are a time series analysis expert. This is a short-duration electromyography (EMG) signal recorded using a concentric needle electrode inserted into the tibialis anterior muscle: <ts><ts/>. The recording captures the amplitude (mV) of electrical activity generated by muscle fibers innervated by motor neurons. This... | Healthy | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The core objective is to classify the EMG signal into one of three neuromuscular conditions (Healthy, Myopathy, or Neuropathy) based on the amplitude values. The background involves electromyography (EMG) data from t... | This is a classification task. | Minimum amplitude; maximum amplitude; trend; fluctuation; continuity | A) healthy | A) healthy | -0.0250, -0.0233, -0.0300, -0.0300, -0.0317, -0.0300, -0.0333, -0.0317, -0.0367, -0.0367, -0.0333, -0.0350, -0.0417, -0.0417, -0.0633, -0.0650, -0.1017, -0.1117, -0.0917, -0.0883, -0.0050, -0.0017, 0.0183, 0.0183, 0.0100, 0.0100, 0.0200, 0.0200, 0.0233, 0.0217, 0.0150, 0.0150, 0.0000, 0.0000, 0.0083, 0.0100, 0.0167, 0.... |
EMG | You are a time series analysis expert. This is a short-duration electromyography (EMG) signal recorded using a concentric needle electrode inserted into the tibialis anterior muscle: <ts><ts/>. The recording captures the amplitude (mV) of electrical activity generated by muscle fibers innervated by motor neurons. This... | Myopathy | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires analyzing an EMG signal's amplitude values to classify it into one of three neuromuscular conditions (Healthy, Myopathy, Neuropathy). Keywords like "choose the best matching label" and the need... | This is a classification task. | Minimum amplitude; maximum amplitude; trend; fluctuation; continuity. | B)myopathy. | B)myopathy | -0.0067, 0.0050, 0.0050, 0.0233, 0.0233, 0.0283, 0.0267, -0.0033, -0.0033, -0.0467, -0.0617, -0.0317, -0.0283, 0.0683, 0.1633, -0.0500, 0.0183, 0.2133, 0.1683, 0.1250, 0.1233, 0.0367, 0.0267, 0.0433, 0.0450, 0.1017, 0.1017, 0.0567, 0.0517, -0.0283, 0.0817, -0.1333, 0.3033, -0.1650, -0.2800, 0.0017, -0.0717, 0.0133, 0.0... |
EMG | You are a time series analysis expert. This is a short-duration electromyography (EMG) signal recorded using a concentric needle electrode inserted into the tibialis anterior muscle: <ts><ts/>. The recording captures the amplitude (mV) of electrical activity generated by muscle fibers innervated by motor neurons. This... | Myopathy | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires labeling an EMG signal as "Healthy," "Myopathy," or "Neuropathy" based on amplitude analysis. Core keywords include: "analyze the approximate minimum and maximum amplitude values," "choose the ... | This is a classification task. | Amplitude range; amplitude distribution; presence of pathological spikes; trend; fluctuation; continuity. | B)myopathy | B)myopathy | -0.0500, -0.0467, -0.0533, -0.0517, -0.0467, -0.0500, -0.0450, -0.0450, -0.0567, -0.0583, -0.0500, -0.0500, -0.0550, -0.0517, -0.0467, -0.0500, -0.0433, -0.0500, -0.0433, -0.0383, -0.0450, -0.0450, -0.0383, -0.0400, -0.0367, -0.0383, -0.0350, -0.0383, -0.0333, -0.0350, -0.0300, -0.0317, -0.0350, -0.0333, -0.0300, -0.03... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | normal sinus rhythm | <THINK>Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying a single-lead ECG time series into one of four predefined rhythm categories ('normal sinus rhythm', 'atrial fibrillation', 'other cardiac rhythms', or 'noise'). The core objective is t... | This is a classification task. | Qrs complex regularity; p wave presence/consistency; qrs morphology stability; signal-to-noise ratio; trend; amplitude; fluctuation; continuity. | A) normal sinus rhythm | A) normal sinus rhythm | -0.1060, -0.1090, -0.1130, -0.1190, -0.1280, -0.1390, -0.1490, -0.1580, -0.1650, -0.1710, -0.1780, -0.1840, -0.1880, -0.1880, -0.1820, -0.1720, -0.1580, -0.1370, -0.1100, -0.0870, -0.0710, -0.0560, -0.0420, -0.0280, -0.0100, 0.0100, 0.0280, 0.0400, 0.0510, 0.0610, 0.0690, 0.0770, 0.0830, 0.0870, 0.0920, 0.0970, 0.1010,... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | noise | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying a single-lead ECG time series into one of four predefined rhythm categories (normal sinus rhythm, atrial fibrillation, other cardiac rhythms, or noise). The core objective is to ana... | This is a classification task. | Rhythm regularity; p wave presence/consistency; qrs complex morphology; baseline noise level; trend; amplitude; fluctuation; continuity. | D) noise. | D) noise | 0.1160, 0.1120, 0.1080, 0.1040, 0.0940, 0.0700, 0.0750, 0.1420, 0.1690, 0.1780, 0.1750, 0.1540, 0.1020, 0.0570, 0.0460, 0.1290, 0.2230, 0.2070, 0.1400, 0.1160, 0.1100, 0.1100, 0.1150, 0.1250, 0.1500, 0.1600, 0.1670, 0.1700, 0.1700, 0.1730, 0.1760, 0.1780, 0.1810, 0.1850, 0.1960, 0.2060, 0.2080, 0.2080, 0.2060, 0.2000, ... |
EMG | You are a time series analysis expert. This is a short-duration electromyography (EMG) signal recorded using a concentric needle electrode inserted into the tibialis anterior muscle: <ts><ts/>. The recording captures the amplitude (mV) of electrical activity generated by muscle fibers innervated by motor neurons. This... | Myopathy | <THINK>Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires analyzing an EMG signal's amplitude characteristics (explicitly stated as "amplitude (mV)") and assigning it to one of three predefined neuromuscular condition labels ("Healthy", "Myopathy", or "Neuro... | This is a classification task. | Minimum amplitude; maximum amplitude; trend; fluctuation; continuity; judgment criteria; threshold values. | B)myopathy | B)myopathy | -0.0567, -0.0533, -0.0117, -0.0550, -0.0283, -0.0283, 0.0650, 0.0917, 0.0383, 0.1067, 0.0483, 0.0350, -0.3250, -0.3983, -0.2450, -0.2267, 0.1183, 0.1217, 0.0183, 0.0067, -0.0633, 0.0100, -0.0483, -0.0650, -0.0400, -0.0367, 0.0483, 0.0467, 0.0133, 0.0067, 0.0283, 0.0500, 0.0150, 0.0100, -0.0733, -0.0633, 0.0317, 0.0500,... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | other cardiac rhythms | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying a single-lead ECG time series into one of four predefined rhythm categories (normal sinus rhythm, atrial fibrillation, other cardiac rhythms, or noise). The core objective is to ass... | This is a classification task. | P-wave presence/absence; qrs complex regularity; rr interval variability; signal-to-noise ratio; trend; amplitude; fluctuation; continuity. | C) other cardiac rhythms. | C) other cardiac rhythms | -0.1490, -0.1430, -0.1400, -0.1370, -0.1300, -0.1240, -0.1210, -0.1190, -0.1200, -0.1200, -0.1270, -0.1470, -0.1610, -0.1670, -0.1730, -0.1740, -0.1720, -0.1670, -0.1600, -0.1530, -0.1370, -0.1270, -0.1200, -0.1200, -0.1340, -0.1510, -0.1580, -0.1630, -0.1690, -0.1660, -0.1540, -0.1460, -0.1410, -0.1390, -0.1370, -0.13... |
RCW | You are analyzing an audio signal to determine the presence of right whale vocalizations.
Up-calls are the most commonly documented right whale vocalisation with anacoustic signature of approximately 60Hz-250Hz, typically lasting 1second.
Right whale calls can often be difficult to hear as the lowfrequency band can b... | No Right Whale | <think>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires determining whether a 2-second audio signal (sampled at 2kHz, length 4000) contains a right whale vocalization ("up-call") based on predefined acoustic characteristics (60–250Hz frequency, ~1-s... | This is a classification task. | Frequency range; duration; amplitude profile; anthropogenic noise interference; trend; fluctuation; continuity. | B) no right whale. | B) no right whale | -0.0030, 0.0803, 0.0085, 0.0021, 0.0586, -0.0189, -0.0967, 0.0742, -0.0250, -0.0378, 0.0021, 0.0201, 0.0357, -0.0549, 0.0369, 0.0052, -0.0586, -0.0427, 0.0189, 0.0922, 0.1004, -0.0775, 0.0424, 0.0351, -0.0177, -0.0314, 0.0778, -0.0296, 0.0372, -0.0537, 0.0568, -0.0217, -0.0696, 0.0067, -0.0287, -0.0110, 0.0015, -0.0095... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | noise | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying a single-lead ECG time series into one of four predefined categories (normal sinus rhythm, atrial fibrillation, other cardiac rhythms, or noise). The core objective is explicitly st... | This is a classification task. | P-wave presence/absence; qrs complex regularity; rr interval variability; signal-to-noise ratio; waveform morphology consistency; trend; amplitude; fluctuation; continuity; judgment criteria; threshold values. | D) noise. | D) noise | -0.7540, -0.7640, -0.8070, -0.8580, -0.9110, -0.9520, -0.9660, -0.9410, -0.8970, -0.8510, -0.8080, -0.7860, -0.7720, -0.7550, -0.7400, -0.7330, -0.7300, -0.7270, -0.7230, -0.7180, -0.7150, -0.7120, -0.7120, -0.7110, -0.7080, -0.7030, -0.6940, -0.6830, -0.6550, -0.6280, -0.6170, -0.6080, -0.6010, -0.5960, -0.5890, -0.57... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | normal sinus rhythm | <THINK>**Step 1. Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem explicitly requires classifying the ECG time series into one of four predefined rhythm categories ("normal sinus rhythm," "atrial fibrillation," "other cardiac rhythms," or "noise"). Keywords like "classify... | This is a classification task. | P wave presence/absence; qrs complex morphology; rhythm regularity; signal noise level; trend; amplitude; fluctuation; continuity; judgment criteria; threshold values | A) normal sinus rhythm. | A) normal sinus rhythm | -0.0570, -0.0700, -0.0760, -0.0800, -0.0830, -0.0840, -0.0830, -0.0780, -0.0710, -0.0640, -0.0540, -0.0440, -0.0330, -0.0240, -0.0160, -0.0090, -0.0030, 0.0030, 0.0120, 0.0210, 0.0320, 0.0560, 0.0810, 0.1050, 0.1410, 0.1740, 0.1970, 0.2240, 0.2410, 0.2500, 0.2470, 0.2330, 0.1920, 0.1390, 0.0820, 0.0340, 0.0070, -0.0150... |
EMG | You are a time series analysis expert. This is a short-duration electromyography (EMG) signal recorded using a concentric needle electrode inserted into the tibialis anterior muscle: <ts><ts/>. The recording captures the amplitude (mV) of electrical activity generated by muscle fibers innervated by motor neurons. This... | Neuropathy | <THINK>**Step 1. Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires assigning the EMG signal to one of three predefined neuromuscular conditions ("Healthy," "Myopathy," or "Neuropathy") based on amplitude characteristics. The core objective is to classify the signa... | This is a classification task. | Minimum amplitude; maximum amplitude; amplitude distribution; trend; fluctuation; continuity; judgment criteria; threshold values. | C)neuropathy. | C)neuropathy | -0.0033, -0.0017, -0.0033, 0.0017, -0.0017, 0.0017, 0.0000, 0.0033, 0.0000, 0.0067, 0.0067, 0.0017, 0.0033, 0.0000, 0.0000, 0.0033, 0.0033, 0.0250, 0.0283, 0.1117, 0.1150, 0.2000, 0.2033, 0.3133, 0.3183, 0.4700, 0.4767, 0.5683, 0.4833, -0.2333, -0.2717, -0.7933, -0.6833, 0.1317, 0.7567, -0.3117, -0.5933, -2.4450, -1.70... |
EMG | You are a time series analysis expert. This is a short-duration electromyography (EMG) signal recorded using a concentric needle electrode inserted into the tibialis anterior muscle: <ts><ts/>. The recording captures the amplitude (mV) of electrical activity generated by muscle fibers innervated by motor neurons. This... | Neuropathy | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The core objective is to classify the given EMG signal into one of three neuromuscular conditions (Healthy, Myopathy, or Neuropathy) based on the analysis of amplitude values. The background involves a short-duration... | This is a classification task. | Minimum amplitude; maximum amplitude; trend; fluctuation; continuity | C)neuropathy | C)neuropathy | -0.0067, -0.0083, -0.0033, -0.0033, -0.0083, -0.0067, -0.0133, -0.0133, -0.0100, -0.0117, -0.0183, -0.0183, -0.0217, -0.0200, -0.0250, -0.0200, -0.0250, -0.0217, -0.0167, -0.0167, -0.0133, -0.0133, -0.0067, -0.0083, 0.0000, 0.0000, 0.0050, 0.0050, 0.0117, 0.0083, 0.0117, 0.0100, 0.0133, 0.0117, 0.0100, 0.0133, 0.0100, ... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | noise | <THINK>Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The core objective is to assign the provided ECG time series data to one of four predefined categories: 'normal sinus rhythm', 'atrial fibrillation', 'other cardiac rhythms', or 'noise'. The problem explicitly states: "... | This is a classification task. | Qrs complex regularity; p wave presence/consistency; baseline stability; amplitude variability; signal-to-noise ratio; trend; fluctuation; continuity. | D) noise. | D) noise | -0.0890, -0.0880, -0.0870, -0.0880, -0.0880, -0.0850, -0.0800, -0.0750, -0.0670, -0.0590, -0.0550, -0.0540, -0.0500, -0.0460, -0.0420, -0.0380, -0.0370, -0.0380, -0.0390, -0.0380, -0.0370, -0.0320, -0.0270, -0.0240, -0.0230, -0.0210, -0.0180, -0.0120, -0.0040, 0.0050, 0.0130, 0.0200, 0.0250, 0.0350, 0.0480, 0.0450, 0.0... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | other cardiac rhythms | <THINK>Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying a single-lead ECG time series into one of four predefined rhythm categories ("normal sinus rhythm," "atrial fibrillation," "other cardiac rhythms," or "noise"). Specific keywords like "... | This is a classification task. | Qrs complex regularity; p wave presence/morphology; rr interval variability; signal-to-noise ratio; trend; amplitude; fluctuation; continuity; judgment criteria; threshold values | C) other cardiac rhythms. | C) other cardiac rhythms | 0.0140, 0.0270, 0.0370, 0.0420, 0.0440, 0.0440, 0.0430, 0.0430, 0.0440, 0.0400, 0.0200, 0.0000, -0.0200, -0.0350, -0.0450, -0.0510, -0.0540, -0.0540, -0.0520, -0.0470, -0.0360, 0.0440, 0.2430, 0.5370, 0.8460, 1.0150, 0.9430, 0.6300, 0.2990, 0.1030, -0.0040, -0.0800, -0.1350, -0.1500, -0.1580, -0.1610, -0.1570, -0.1480,... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | normal sinus rhythm | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The core objective is to classify a single-lead ECG time series into one of four predefined rhythm categories (normal sinus rhythm, atrial fibrillation, other cardiac rhythms, or noise). The problem specifies analy... | This is a classification task. | P wave presence/absence; qrs complex regularity; rr interval consistency; signal noise level; trend; fluctuation; continuity. | A) normal sinus rhythm. | A) normal sinus rhythm | -0.0200, -0.0160, -0.0120, -0.0070, 0.0000, 0.0040, 0.0000, -0.0120, -0.0450, -0.0720, -0.0860, -0.0970, -0.1020, -0.1010, -0.0990, -0.0960, -0.0950, -0.0970, -0.1000, -0.1040, -0.0980, -0.0740, -0.0030, 0.1530, 0.3880, 0.5710, 0.5100, 0.1220, -0.3280, -0.5300, -0.4930, -0.3200, -0.1680, -0.1000, -0.0830, -0.0720, -0.0... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | normal sinus rhythm | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying an ECG time series into one of four predefined rhythm categories (normal sinus rhythm, atrial fibrillation, other cardiac rhythms, or noise). Specific keywords like "classify the he... | This is a classification task. | Qrs complex regularity; p-wave presence/absence; r-r interval variability; signal-to-noise ratio; trend; amplitude; fluctuation; continuity; judgment criteria; threshold values | A) normal sinus rhythm. | A) normal sinus rhythm | 0.7250, 0.5170, -0.0770, -0.6220, -0.7260, -0.5770, -0.4190, -0.3110, -0.1910, -0.0890, -0.0400, -0.0200, -0.0130, -0.0080, -0.0020, 0.0020, 0.0070, 0.0140, 0.0230, 0.0340, 0.0450, 0.0590, 0.0720, 0.0850, 0.0990, 0.1130, 0.1240, 0.1360, 0.1500, 0.1710, 0.2020, 0.2250, 0.2450, 0.2560, 0.2570, 0.2440, 0.2230, 0.2010, 0.1... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | atrial fibrillation | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying a single-lead ECG time series into one of four predefined rhythm categories: 'normal sinus rhythm', 'atrial fibrillation', 'other cardiac rhythms', or 'noise'. The core objective is... | This is a classification task. | Rhythm regularity; p-wave presence/absence; qrs complex morphology consistency; signal-to-noise ratio; trend; amplitude; fluctuation; continuity. | B) atrial fibrillation. | B) atrial fibrillation | -0.0490, -0.0480, -0.0450, -0.0420, -0.0380, -0.0350, -0.0390, -0.0450, -0.0550, -0.0560, -0.0300, 0.0780, 0.2430, 0.3430, 0.2230, -0.0690, -0.3230, -0.3440, -0.2350, -0.1550, -0.0980, -0.0820, -0.0700, -0.0570, -0.0380, -0.0130, 0.0110, 0.0190, 0.0110, -0.0100, -0.0250, -0.0250, -0.0100, 0.0190, 0.0570, 0.0850, 0.0990... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | other cardiac rhythms | <THINK>**Step 1. Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying a single-lead ECG time series into one of four predefined rhythm categories (normal sinus rhythm, atrial fibrillation, other cardiac rhythms, or noise). The core objective is to analyze... | This is a classification task. | Qrs complex morphology; p wave presence/consistency; rhythm regularity; signal noise level; trend; amplitude; fluctuation; continuity; judgment criteria; threshold values. | C) other cardiac rhythms. | C) other cardiac rhythms | -0.0380, -0.0050, 0.0950, 0.1320, 0.1460, 0.1760, 0.2120, 0.1820, 0.0580, 0.0310, 0.0810, 0.0890, 0.0730, 0.0630, 0.0620, 0.0660, 0.0680, 0.0680, 0.0680, 0.0680, 0.0650, 0.0560, 0.0370, 0.0100, 0.0020, 0.0000, 0.0040, 0.0130, 0.0220, 0.0310, 0.0350, 0.0380, 0.0420, 0.0370, 0.0200, 0.0070, 0.0020, 0.0020, 0.0110, 0.0350... |
EMG | You are a time series analysis expert. This is a short-duration electromyography (EMG) signal recorded using a concentric needle electrode inserted into the tibialis anterior muscle: <ts><ts/>. The recording captures the amplitude (mV) of electrical activity generated by muscle fibers innervated by motor neurons. This... | Neuropathy | <THINK>**Step 1. Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying an EMG signal into one of three neuromuscular conditions (Healthy, Myopathy, Neuropathy) based on amplitude characteristics. The core objective is to infer the condition from the signal... | This is a classification task. | Minimum amplitude; maximum amplitude; trend; fluctuation; continuity. | C)neuropathy | C)neuropathy | 0.0167, 0.0217, 0.0183, 0.0217, 0.0217, 0.0183, 0.0200, 0.0133, 0.0167, 0.0150, 0.0167, 0.0133, 0.0133, 0.0167, 0.0183, 0.0133, 0.0150, 0.0200, 0.0183, 0.0150, 0.0167, 0.0100, 0.0117, -0.0067, -0.0050, -0.0150, -0.0117, -0.0200, -0.0150, -0.0200, -0.0200, -0.0150, -0.0133, -0.0017, -0.0017, 0.0100, 0.0083, 0.0117, 0.01... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | atrial fibrillation | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying a single-lead ECG time series into one of four predefined rhythm categories (normal sinus rhythm, atrial fibrillation, other cardiac rhythms, or noise). Keywords like "classify the ... | This is a classification task. | Irregular r-r intervals; absence of p waves; irregular baseline fluctuations; consistent qrs morphology; trend; fluctuation. | B) atrial fibrillation | B) atrial fibrillation | -0.0420, -0.0280, -0.0190, -0.0110, -0.0050, 0.0000, 0.0050, 0.0170, 0.0430, 0.0580, 0.0760, 0.0960, 0.1080, 0.1140, 0.1160, 0.1110, 0.1000, 0.0700, 0.0470, 0.0370, 0.0250, 0.0060, -0.0050, -0.0210, -0.0420, -0.0510, -0.0540, -0.0540, -0.0560, -0.0590, -0.0600, -0.0630, -0.0660, -0.0670, -0.0700, -0.0760, -0.0760, -0.0... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | other cardiac rhythms | <THINK>**Step 1. Analyzing task intent:**
[Judgment] This is a classification task.
[Description] The problem requires classifying a single-lead ECG time series into one of four predefined rhythm categories ("normal sinus rhythm," "atrial fibrillation," "other cardiac rhythms," or "noise"). The core objective is to... | This is a classification task. | Qrs complex regularity; p wave presence/consistency; rr interval variability; signal-to-noise ratio; trend; amplitude; fluctuation; continuity; judgment criteria; threshold values. | C) other cardiac rhythms. | C) other cardiac rhythms | -0.4830, -0.3590, -0.2350, -0.2390, -0.4450, -0.8440, -1.0890, -1.0690, -0.9560, -0.8850, -0.8490, -0.8220, -0.7880, -0.7400, -0.6880, -0.6380, -0.5900, -0.5520, -0.5180, -0.4880, -0.4710, -0.4630, -0.4600, -0.4530, -0.4490, -0.4430, -0.4300, -0.4040, -0.3780, -0.3480, -0.2970, -0.2600, -0.2200, -0.1810, -0.1450, -0.10... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | normal sinus rhythm | <THINK>**Step 1. Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The core objective is to classify a single-lead ECG time series into one of four predefined rhythm categories: normal sinus rhythm, atrial fibrillation, other cardiac rhythms, or noise. The problem explicitly states, "... | This is a classification task. | Rhythm regularity; p-wave presence/morphology; qrs complex characteristics; signal-to-noise ratio; trend; amplitude; fluctuation; continuity. | A) normal sinus rhythm | A) normal sinus rhythm | 0.1500, 0.1590, 0.1640, 0.1690, 0.1720, 0.1740, 0.1720, 0.1640, 0.1520, 0.1290, 0.1010, 0.0820, 0.0690, 0.0580, 0.0490, 0.0440, 0.0400, 0.0370, 0.0330, 0.0310, 0.0310, 0.0360, 0.0440, 0.0520, 0.0590, 0.0630, 0.0660, 0.0690, 0.0720, 0.0750, 0.0770, 0.0780, 0.0790, 0.0790, 0.0790, 0.0800, 0.0800, 0.0790, 0.0780, 0.0770, ... |
EMG | You are a time series analysis expert. This is a short-duration electromyography (EMG) signal recorded using a concentric needle electrode inserted into the tibialis anterior muscle: <ts><ts/>. The recording captures the amplitude (mV) of electrical activity generated by muscle fibers innervated by motor neurons. This... | Myopathy | <THINK>**Step 1. Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires analyzing EMG signal amplitudes to classify it into one of three neuromuscular conditions (Healthy, Myopathy, Neuropathy). Specific keywords like "choose the best matching label" and the provided o... | This is a classification task. | Amplitude range; Trend; Fluctuation; Continuity; Judgment criteria; Threshold values | B)myopathy | B)myopathy | 0.0083, 0.0033, 0.0050, -0.0033, -0.0050, -0.0150, -0.0150, -0.0100, -0.0100, 0.0167, 0.0167, 0.0767, 0.0900, 0.0533, 0.0483, -0.0200, 0.0383, -0.1383, -0.0850, 0.2200, 0.1867, 0.4000, 0.4083, 0.0967, 0.1000, 0.4117, 0.4583, 0.3683, 0.3617, 0.1483, 0.1400, -0.0817, -0.0717, 0.0283, 0.0483, 0.0133, 0.0117, -0.0267, -0.0... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | noise | <THINK>Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem explicitly requires classifying the ECG rhythm into one of four predefined categories ("normal sinus rhythm," "atrial fibrillation," "other cardiac rhythms," or "noise"). The core objective is to infer the ... | This is a classification task. | Rhythm regularity; p-wave presence; qrs complex morphology; signal-to-noise ratio; amplitude stability; trend; fluctuation; continuity. | D) noise | D) noise | 0.0660, 0.1690, 0.2250, 0.2360, 0.2460, 0.2630, 0.2880, 0.2950, 0.3100, 0.3380, 0.3380, 0.3660, 0.3900, 0.3980, 0.4370, 0.4690, 0.4960, 0.5320, 0.5530, 0.5510, 0.5630, 0.5620, 0.5540, 0.5630, 0.5620, 0.5540, 0.5460, 0.5360, 0.5160, 0.5890, 0.7940, 1.1530, 1.5750, 1.8460, 1.8550, 1.6630, 1.3590, 1.1440, 1.0320, 1.0200, ... |
EMG | You are a time series analysis expert. This is a short-duration electromyography (EMG) signal recorded using a concentric needle electrode inserted into the tibialis anterior muscle: <ts><ts/>. The recording captures the amplitude (mV) of electrical activity generated by muscle fibers innervated by motor neurons. This... | Neuropathy | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires analyzing an EMG signal's amplitude values to classify it into one of three neuromuscular conditions (Healthy, Myopathy, Neuropathy). Keywords like "choose the best matching label" and the provis... | This is a classification task. | Minimum amplitude; maximum amplitude; trend; fluctuation; continuity | C)neuropathy | C)neuropathy | -0.0033, -0.3600, 0.7017, 0.5300, -2.3533, -2.1633, -0.7950, -0.7150, 0.2150, 0.2267, 0.4883, 0.4933, 0.4533, 0.4483, 0.3450, 0.3417, 0.2583, 0.2567, 0.1583, 0.1550, 0.1083, 0.1083, 0.0900, 0.0883, 0.0733, 0.0717, 0.0533, 0.0517, 0.0367, 0.0367, 0.0217, 0.0200, 0.0017, 0.0000, -0.0100, -0.0100, -0.0150, -0.0133, -0.006... |
RCW | You are analyzing an audio signal to determine the presence of right whale vocalizations.
Up-calls are the most commonly documented right whale vocalisation with anacoustic signature of approximately 60Hz-250Hz, typically lasting 1second.
Right whale calls can often be difficult to hear as the lowfrequency band can b... | No Right Whale | <think>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires determining whether a given 2-second audio signal (sampled at 2kHz, length 4000) contains a right whale vocalization. Core keywords include "classify whether the waveform segment contains a rig... | This is a classification task. | Frequency range; duration; amplitude consistency; noise interference thresholds; trend; fluctuation; continuity. | B) no right whale. | B) no right whale | -0.0604, 0.1184, -0.1526, -0.0528, -0.0366, -0.0089, -0.0220, 0.0247, -0.0250, -0.0018, 0.1004, 0.0287, -0.0122, -0.1773, 0.0439, 0.1157, 0.0525, -0.1926, -0.0720, 0.0781, 0.1364, 0.0699, -0.0241, 0.0293, 0.0006, 0.0854, -0.0497, 0.0223, 0.1764, -0.1083, 0.0525, -0.0272, -0.1569, 0.0610, 0.0653, -0.0104, 0.1703, -0.016... |
EMG | You are a time series analysis expert. This is a short-duration electromyography (EMG) signal recorded using a concentric needle electrode inserted into the tibialis anterior muscle: <ts><ts/>. The recording captures the amplitude (mV) of electrical activity generated by muscle fibers innervated by motor neurons. This... | Neuropathy | <THINK>**Step 1. Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires categorizing an EMG signal into one of three neuromuscular conditions (Healthy, Myopathy, Neuropathy) based on amplitude analysis. Key requirements include: (1) analyzing "amplitude (mV) of electri... | This is a classification task. | Amplitude range; amplitude extremes (min/max); signal variability; trend; continuity. | C)neuropathy | C)neuropathy | -0.0350, 0.2583, 0.2933, 2.0517, 0.9300, -2.6733, -2.7050, -0.8250, -0.4083, -0.9033, -0.8983, -0.2550, -0.2283, 0.5200, 0.5450, 0.9500, 0.9633, 0.7633, 0.7467, 0.1600, 0.1250, -0.9767, -0.8783, -0.3300, -0.3067, 0.1233, 0.1400, 0.4133, 0.4183, 0.4650, 0.4200, 0.1417, 0.1250, -0.0867, -0.0867, 0.0500, 0.0567, 0.2267, 0... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | atrial fibrillation | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The core objective is to classify a single-lead ECG time series into one of four predefined rhythm categories (normal sinus rhythm, atrial fibrillation, other cardiac rhythms, or noise). The problem explicitly requ... | This is a classification task. | P-wave presence/absence; qrs complex regularity; r-r interval variability; baseline stability; noise artifacts; trend; amplitude; fluctuation; continuity. | B) atrial fibrillation | B) atrial fibrillation | -0.2710, -0.2460, -0.1860, -0.1250, -0.1130, -0.1110, -0.1110, -0.1110, -0.1100, -0.1050, -0.0910, -0.0590, -0.0140, 0.0350, 0.0850, 0.1160, 0.1380, 0.1660, 0.1790, 0.1840, 0.1840, 0.1800, 0.1680, 0.1380, 0.1100, 0.0950, 0.0620, -0.1080, -0.2500, -0.2780, -0.2560, -0.2080, -0.0480, 0.0320, 0.0090, -0.0120, -0.0220, -0.... |
EMG | You are a time series analysis expert. This is a short-duration electromyography (EMG) signal recorded using a concentric needle electrode inserted into the tibialis anterior muscle: <ts><ts/>. The recording captures the amplitude (mV) of electrical activity generated by muscle fibers innervated by motor neurons. This... | Neuropathy | <THINK>**Step 1. Analyzing task intent:**
[Judgment] This is a classification task.
[Description] The problem requires analyzing an EMG signal's amplitude characteristics (specifically minimum and maximum values) to classify it into one of three neuromuscular conditions: Healthy, Myopathy, or Neuropathy. Keywords ... | This is a classification task. | Maximum amplitude value; trend; fluctuation; continuity | C)neuropathy. | C)neuropathy | -0.4517, -0.4500, -0.4217, -0.4200, -0.4133, -0.4133, -0.4200, -0.4200, -0.4150, -0.4133, -0.4183, -0.4150, -0.4183, -0.4150, -0.3867, -0.3867, -0.3383, -0.3350, -0.2783, -0.2750, -0.2317, -0.2283, -0.1850, -0.1817, -0.1300, -0.1283, -0.0683, -0.0667, -0.0067, -0.0033, 0.0917, 0.0967, 0.3183, 0.3317, 0.8433, 0.9600, 0.... |
RCW | You are analyzing an audio signal to determine the presence of right whale vocalizations.
Up-calls are the most commonly documented right whale vocalisation with anacoustic signature of approximately 60Hz-250Hz, typically lasting 1second.
Right whale calls can often be difficult to hear as the lowfrequency band can b... | No Right Whale | <think>**Step 1. Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem explicitly requires classifying whether the audio signal contains a right whale vocalization based on predefined criteria (60–250 Hz frequency band, ~1-second duration). The core objective is binary: determ... | This is a classification task. | Frequency band; duration; amplitude consistency; anthropogenic noise interference; trend; fluctuation; continuity. | B)no right whale. | B)no right whale | -0.0043, -0.0211, 0.0510, 0.0449, -0.0195, 0.0259, 0.0058, -0.0061, 0.0412, 0.0052, 0.0012, 0.0253, -0.0415, -0.0125, 0.0003, 0.0149, 0.0134, -0.0156, 0.0104, 0.0247, -0.0110, 0.0381, 0.0085, 0.0018, 0.0333, -0.0137, -0.0149, 0.0183, 0.0070, -0.0333, -0.0357, -0.0137, 0.0028, -0.0400, 0.0552, 0.0208, 0.0220, 0.0171, 0.... |
CTU | You are a time series analysis expert. This is a time series signal derived from electricity usage patterns in UK households, recorded as part of the government-sponsored study "Powering the Nation": <ts><ts/>. The signal represents energy consumption sampled every 2 minutes over a 24-hour period, resulting in a series... | Laptop | <THINK>Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The core objective is to classify a household's device usage pattern as either "Desktop" or "Laptop" based on electricity consumption time series data. Specific keywords include "classify the household’s device usage ... | This is a classification task. | Power amplitude baseline; burst pattern amplitude/duration; negative consumption occurrence; low-consumption duration; volatility; trend; fluctuation; continuity. | B)laptop | B)laptop | -0.6489, -0.2440, -0.6489, -0.2440, -0.6489, -0.2440, -0.2440, -0.6489, -0.2440, -0.6489, -0.2440, -0.6489, -0.2440, -0.6489, -0.2440, -0.6489, -0.2440, -0.6489, -0.2440, -0.6489, -0.2440, -0.2440, -0.6489, -0.2440, -0.6489, -0.2440, -0.6489, -0.2440, -0.6489, -0.2440, -0.6489, 0.5657, -0.6489, -0.6489, -0.2440, -0.648... |
EMG | You are a time series analysis expert. This is a short-duration electromyography (EMG) signal recorded using a concentric needle electrode inserted into the tibialis anterior muscle: <ts><ts/>. The recording captures the amplitude (mV) of electrical activity generated by muscle fibers innervated by motor neurons. This... | Neuropathy | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires analyzing an EMG signal's amplitude characteristics (minimum and maximum values) to classify it into one of three neuromuscular conditions: "Healthy," "Myopathy," or "Neuropathy." The core obje... | This is a classification task. | Minimum amplitude; maximum amplitude; amplitude range; trend; fluctuation; continuity; threshold values; judgment criteria. | C)neuropathy | C)neuropathy | 0.0217, 0.0300, 0.0300, 0.0367, 0.0333, 0.0417, 0.0383, 0.0433, 0.0433, 0.0367, 0.0383, 0.0333, 0.0350, 0.0267, 0.0267, 0.0217, 0.0233, 0.0183, 0.0200, 0.0117, 0.0117, 0.0017, -0.0017, 0.0033, 0.0050, 0.0250, 0.0267, 0.0433, 0.0433, 0.0483, 0.0483, 0.0217, 0.0200, -0.0483, -0.0517, -0.1550, -0.1583, -0.2117, -0.2167, -... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | normal sinus rhythm | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem explicitly requires classifying a single-lead ECG time series into one of four predefined rhythm categories: 'normal sinus rhythm', 'atrial fibrillation', 'other cardiac rhythms', or 'noise'. Keywords l... | This is a classification task. | Rhythm regularity; p-wave presence/morphology; qrs complex morphology; signal-to-noise ratio (snr); trend; fluctuation; continuity | A) normal sinus rhythm. | A) normal sinus rhythm | 0.0370, 0.1800, 0.3700, 0.4950, 0.4440, 0.1790, -0.0970, -0.2320, -0.2460, -0.1880, -0.0890, -0.0460, -0.0330, -0.0250, -0.0190, -0.0170, -0.0150, -0.0140, -0.0140, -0.0140, -0.0140, -0.0120, -0.0100, -0.0070, -0.0020, 0.0010, 0.0160, 0.0320, 0.0430, 0.0500, 0.0500, 0.0450, 0.0410, 0.0790, 0.1100, 0.1180, 0.1220, 0.130... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | other cardiac rhythms | <THINK>**Step 1. Analyzing task intent:**
[Judgment] This is a classification task.
[Description] The problem explicitly requires classifying the ECG rhythm into one of four predefined categories ("normal sinus rhythm," "atrial fibrillation," "other cardiac rhythms," or "noise"). Keywords like "classify the heart r... | This is a classification task. | Qrs complex morphology; p-wave presence/consistency; rr interval regularity; signal-to-noise ratio; trend; amplitude; fluctuation; continuity | C) other cardiac rhythms. | C) other cardiac rhythms | -0.0630, -0.0450, -0.0310, 0.0040, 0.0150, 0.0220, 0.0230, 0.0210, 0.0190, 0.0160, 0.0100, 0.0100, 0.0110, 0.0120, 0.0140, 0.0160, 0.0160, 0.0140, 0.0130, 0.0110, 0.0080, 0.0080, 0.0090, 0.0080, 0.0080, 0.0070, 0.0070, 0.0130, 0.0150, 0.0180, 0.0210, 0.0250, 0.0270, 0.0280, 0.0280, 0.0280, 0.0290, 0.0300, 0.0310, 0.031... |
EMG | You are a time series analysis expert. This is a short-duration electromyography (EMG) signal recorded using a concentric needle electrode inserted into the tibialis anterior muscle: <ts><ts/>. The recording captures the amplitude (mV) of electrical activity generated by muscle fibers innervated by motor neurons. This... | Myopathy | <THINK>**Step 1. Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires assigning the EMG signal to one of three predefined neuromuscular condition labels ("Healthy", "Myopathy", "Neuropathy") based on analysis of its amplitude characteristics. The core objective is to in... | This is a classification task. | Minimum amplitude; maximum amplitude; trend; fluctuation; continuity. | B)myopathy. | B)myopathy | -0.0133, -0.0117, -0.0083, -0.0100, -0.0133, -0.0117, -0.0050, -0.0050, 0.0083, 0.0083, 0.0233, 0.0250, 0.0367, 0.0367, 0.0533, 0.0533, 0.0800, 0.0817, 0.1150, 0.1183, 0.1650, 0.1650, 0.0567, 0.0433, -0.3733, -0.3867, -0.4800, -0.3967, -0.1200, -0.1083, -0.0433, -0.0717, -0.1500, -0.1333, 0.0083, 0.0067, -0.0533, -0.02... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | atrial fibrillation | <THINK>**Step 1. Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying a single-lead ECG time series into one of four predefined rhythm categories ("normal sinus rhythm," "atrial fibrillation," "other cardiac rhythms," or "noise"). Keywords like "classify ... | This is a classification task. | R-r interval irregularity; absence of p waves; baseline irregularity; qrs morphology consistency; trend; amplitude; fluctuation; continuity. | B) atrial fibrillation | B) atrial fibrillation | -0.0520, -0.0410, -0.0320, -0.0430, -0.0400, -0.0360, -0.0320, -0.0290, -0.0280, -0.0270, -0.0270, -0.0250, -0.0230, -0.0190, -0.0150, -0.0130, -0.0120, -0.0130, -0.0150, -0.0180, -0.0200, -0.0240, -0.0270, -0.0310, -0.0350, -0.0410, -0.0470, -0.0520, -0.0550, -0.0560, -0.0510, -0.0460, -0.0410, -0.0370, -0.0320, -0.02... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | normal sinus rhythm | <THINK>Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying a single-lead ECG time series into one of four predefined rhythm categories: 'normal sinus rhythm', 'atrial fibrillation', 'other cardiac rhythms', or 'noise'. The core objective is pa... | This is a classification task. | P-wave presence/consistency; qrs complex regularity; baseline stability; absence of fibrillatory waves; physiological amplitude range; trend; amplitude; fluctuation; continuity; threshold values. | A) normal sinus rhythm | A) normal sinus rhythm | 0.0690, 0.0680, 0.0640, 0.0600, 0.0530, 0.0480, 0.0640, 0.0850, 0.1120, 0.1470, 0.1740, 0.1920, 0.2060, 0.2140, 0.2200, 0.2240, 0.2240, 0.2230, 0.2180, 0.2110, 0.2060, 0.2010, 0.1910, 0.1830, 0.1830, 0.1900, 0.2010, 0.2210, 0.2320, 0.2350, 0.2390, 0.2390, 0.2310, 0.2260, 0.2230, 0.2180, 0.2140, 0.2100, 0.2050, 0.2010, ... |
CTU | You are a time series analysis expert. This is a time series signal derived from electricity usage patterns in UK households, recorded as part of the government-sponsored study "Powering the Nation": <ts><ts/>. The signal represents energy consumption sampled every 2 minutes over a 24-hour period, resulting in a series... | Laptop | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires labeling the energy consumption signal as either "Desktop" or "Laptop" based on patterns. Specific keywords: *"classify the household’s device usage pattern"* and *"choose the best matching lab... | This is a classification task. | Amplitude; fluctuation; continuity/intermittency; temporal distribution of peaks; trend; judgment criteria; threshold values. | B) laptop | B) laptop | -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.2334, -0.23... |
EMG | You are a time series analysis expert. This is a short-duration electromyography (EMG) signal recorded using a concentric needle electrode inserted into the tibialis anterior muscle: <ts><ts/>. The recording captures the amplitude (mV) of electrical activity generated by muscle fibers innervated by motor neurons. This... | Neuropathy | <THINK>**Step 1. Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying an EMG signal into one of three neuromuscular conditions (Healthy, Myopathy, Neuropathy) based on amplitude characteristics. The core objective is to infer the condition from the signal... | This is a classification task. | Minimum amplitude; maximum amplitude; trend; fluctuation; continuity. | C)neuropathy | C)neuropathy | -0.0433, -0.0417, -0.0083, -0.0083, 0.0133, 0.0133, 0.0183, 0.0183, 0.0117, 0.0100, -0.0033, -0.0033, -0.0083, -0.0083, 0.0017, 0.0000, 0.0067, 0.0083, 0.0150, 0.0150, 0.0233, 0.0217, 0.0333, 0.0333, 0.0433, 0.0433, 0.0517, 0.0500, 0.0567, 0.0533, 0.0267, 0.0250, -0.0033, -0.0050, -0.0167, -0.0167, -0.0083, -0.0117, -0... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | normal sinus rhythm | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying a single-lead ECG time series into one of four predefined rhythm categories (normal sinus rhythm, atrial fibrillation, other cardiac rhythms, or noise). The core objective is to ide... | This is a classification task. | P wave presence/consistency; qrs complex regularity; rr interval variability; signal noise amplitude; trend; fluctuation; continuity. | A) normal sinus rhythm | A) normal sinus rhythm | 0.0770, -0.1040, -0.1900, -0.2000, -0.1350, -0.0300, 0.0000, 0.0030, 0.0070, 0.0090, 0.0080, 0.0090, 0.0140, 0.0180, 0.0230, 0.0290, 0.0350, 0.0410, 0.0520, 0.0640, 0.0670, 0.0660, 0.0650, 0.0620, 0.0580, 0.0560, 0.0590, 0.0690, 0.0820, 0.1010, 0.1170, 0.1340, 0.1530, 0.1720, 0.1890, 0.2050, 0.2230, 0.2380, 0.2510, 0.2... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | other cardiac rhythms | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying an ECG time series into one of four predefined rhythm categories (normal sinus rhythm, atrial fibrillation, other cardiac rhythms, or noise). Specific keywords include: "classify th... | This is a classification task. | P-wave presence/regularity; qrs complex regularity; r-r interval variability; signal-to-noise ratio; waveform morphology consistency; trend; amplitude; fluctuation; continuity; judgment criteria; threshold values. | C) other cardiac rhythms | C) other cardiac rhythms | 0.0830, 0.0930, 0.0990, 0.0950, 0.0890, 0.0890, 0.0970, 0.1010, 0.0600, -0.0720, -0.2920, -0.4800, -0.4360, -0.0710, 0.3040, 0.3500, 0.2010, 0.1420, 0.1290, 0.1190, 0.1120, 0.1050, 0.1000, 0.0960, 0.0920, 0.0850, 0.0790, 0.0750, 0.0680, 0.0580, 0.0470, 0.0350, 0.0230, 0.0150, 0.0080, -0.0040, -0.0200, -0.0410, -0.0730,... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | normal sinus rhythm | <THINK>**Step 1. Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem explicitly requires classifying a single-lead ECG time series into one of four predefined rhythm categories: "normal sinus rhythm," "atrial fibrillation," "other cardiac rhythms," or "noise." Keywords like ... | This is a classification task. | Regularity of r-r intervals; presence of p-waves; qrs complex morphology consistency; signal-to-noise ratio; trend; amplitude; fluctuation; continuity. | A) normal sinus rhythm | A) normal sinus rhythm | 0.1610, 0.1660, 0.1750, 0.1850, 0.1930, 0.2000, 0.2040, 0.2060, 0.2090, 0.2130, 0.2180, 0.2230, 0.2290, 0.2370, 0.2490, 0.2730, 0.3000, 0.3220, 0.3340, 0.3380, 0.3390, 0.3410, 0.3430, 0.3420, 0.3360, 0.3240, 0.3070, 0.2740, 0.2360, 0.2010, 0.1720, 0.1330, 0.0910, 0.0490, 0.0040, -0.0410, -0.0770, -0.0970, -0.1150, -0.1... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | normal sinus rhythm | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying a single-lead ECG time series into one of four predefined rhythm categories (normal sinus rhythm, atrial fibrillation, other cardiac rhythms, or noise). The core objective is to ass... | This is a classification task. | P wave presence/absence; r-r interval regularity; qrs complex morphology consistency; signal-to-noise ratio; trend; amplitude; fluctuation; continuity; judgment criteria; threshold values. | A) normal sinus rhythm. | A) normal sinus rhythm | -0.0010, -0.0070, -0.0120, -0.0180, -0.0210, -0.0240, -0.0250, -0.0270, -0.0270, -0.0280, -0.0290, -0.0290, -0.0310, -0.0330, -0.0360, -0.0400, -0.0350, -0.0110, 0.0170, 0.0400, 0.0540, 0.0680, 0.0640, 0.0390, 0.0220, 0.0080, -0.0020, -0.0120, -0.0190, -0.0230, -0.0270, -0.0320, -0.0350, -0.0380, -0.0420, -0.0450, -0.0... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | noise | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying a single-lead ECG time series into one of four predefined rhythm categories (normal sinus rhythm, atrial fibrillation, other cardiac rhythms, or noise). The core objective is to ass... | This is a classification task. | Rhythm regularity; p-wave presence; qrs complex morphology; signal-to-noise ratio; trend; amplitude; fluctuation; continuity. | D) noise. | D) noise | -0.0160, -0.0100, -0.0040, 0.0030, 0.0120, 0.0160, 0.0180, 0.0220, 0.0260, 0.0280, 0.0300, 0.0310, 0.0310, 0.0320, 0.0330, 0.0340, 0.0340, 0.0320, 0.0300, 0.0300, 0.0330, 0.0360, 0.0390, 0.0430, 0.0500, 0.0690, 0.1450, 0.2530, 0.3600, 0.3640, 0.2280, 0.0180, -0.0950, -0.1050, -0.0600, 0.0130, 0.0550, 0.0750, 0.0840, 0.... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | noise | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying a single-lead ECG time series into one of four predefined rhythm categories (normal sinus rhythm, atrial fibrillation, other cardiac rhythms, or noise). The core objective is to ass... | This is a classification task. | Amplitude variability; absence of p waves; irregular qrs intervals; baseline instability; signal-to-noise ratio; trend; fluctuation; continuity. | D) noise. | D) noise | -0.1640, 0.2580, -0.1340, -0.3600, -0.4660, -0.0890, -0.3570, -0.3070, -0.5010, -0.6950, -0.6640, -0.4690, -0.5860, -0.4600, -0.2480, -0.3750, -0.0360, 0.0820, 0.2750, 0.4390, 0.1040, 0.5160, 0.5040, 0.4570, 0.5190, 0.1460, -0.0160, -0.4100, -0.4280, -0.3730, -0.0900, 0.0580, -0.2640, -0.6000, -0.8190, -0.3680, 0.4030,... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | atrial fibrillation | <THINK>**Step 1. Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying a single-lead ECG time series into one of four predefined rhythm categories: 'normal sinus rhythm', 'atrial fibrillation', 'other cardiac rhythms', or 'noise'. The core objective is to ... | This is a classification task. | Absence of p waves; irregularly irregular r-r intervals; presence of fibrillatory waves; qrs morphology consistency; trend; amplitude; fluctuation; continuity. | B) atrial fibrillation | B) atrial fibrillation | 0.1060, 0.1180, 0.1140, 0.1000, 0.0720, 0.0390, 0.0060, -0.0180, -0.0430, -0.0700, -0.0910, -0.1100, -0.1180, -0.1200, -0.1210, -0.1220, -0.1230, -0.1220, -0.1200, -0.1150, -0.1080, -0.1010, -0.0920, -0.0830, -0.0760, -0.0720, -0.0690, -0.0660, -0.0630, -0.0590, -0.0540, -0.0500, -0.0450, -0.0380, -0.0240, -0.0210, -0.... |
EMG | You are a time series analysis expert. This is a short-duration electromyography (EMG) signal recorded using a concentric needle electrode inserted into the tibialis anterior muscle: <ts><ts/>. The recording captures the amplitude (mV) of electrical activity generated by muscle fibers innervated by motor neurons. This... | Neuropathy | <THINK>**Step 1. Analyzing task intent:**
[Judgment] This is a classification task.
[Description] The problem requires assigning the EMG signal to one of three predefined labels ("Healthy", "Myopathy", or "Neuropathy") based on amplitude characteristics. The core objective is to categorize the neuromuscular condition... | This is a classification task. | Amplitude range; amplitude distribution; trend; fluctuation; continuity; judgment criteria; threshold values. | C)neuropathy | C)neuropathy | -0.0250, -0.0233, -0.0283, -0.0250, -0.0300, -0.0300, -0.0267, -0.0283, -0.0350, -0.0333, 0.0183, 0.0233, 0.1017, 0.1050, 0.2050, 0.2100, 0.3417, 0.3483, 0.5100, 0.5083, 0.0800, 0.0417, -0.7417, -0.7767, -0.0217, -0.3783, 0.6500, 0.5217, -2.4550, -2.2367, -0.7467, -0.6700, 0.1967, 0.2117, 0.4683, 0.4750, 0.4250, 0.4217... |
RCW | You are analyzing an audio signal to determine the presence of right whale vocalizations.
Up-calls are the most commonly documented right whale vocalisation with anacoustic signature of approximately 60Hz-250Hz, typically lasting 1second.
Right whale calls can often be difficult to hear as the lowfrequency band can b... | No Right Whale | <think>Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem explicitly requires determining whether a specific acoustic signal "contains a right whale vocalization" by choosing between two discrete labels: "Right Whale Present" or "No Right Whale." The background in... | This is a classification task. | Spectral energy concentration in 60–250hz band; sustained duration of ~1 second; amplitude consistency during vocalization; trend; fluctuation; continuity. | B) no right whale. | B) no right whale | 0.1535, 0.0519, 0.0116, -0.1129, 0.0174, -0.0391, 0.0269, -0.0861, -0.0726, 0.0052, -0.0189, -0.0470, -0.0052, 0.0043, -0.0140, -0.0320, -0.0461, -0.0247, -0.0134, 0.0796, -0.0430, 0.0351, -0.0067, 0.0180, -0.0684, 0.0394, 0.0391, -0.0803, -0.0500, -0.0372, 0.0656, 0.0449, -0.0986, -0.0384, -0.1230, -0.0632, -0.0159, -... |
EMG | You are a time series analysis expert. This is a short-duration electromyography (EMG) signal recorded using a concentric needle electrode inserted into the tibialis anterior muscle: <ts><ts/>. The recording captures the amplitude (mV) of electrical activity generated by muscle fibers innervated by motor neurons. This... | Myopathy | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires labeling an EMG signal as "Healthy," "Myopathy," or "Neuropathy" based on amplitude analysis. Key phrases like "choose the best matching label" and the provision of three discrete options (a/b/... | This is a classification task. | Minimum amplitude; maximum amplitude; trend; fluctuation; continuity | B)myopathy. | B)myopathy | -0.0017, -0.0117, -0.0067, -0.0267, -0.0267, -0.0183, -0.0217, -0.0167, -0.0200, -0.0300, -0.0300, -0.0383, -0.0383, -0.0500, -0.0483, -0.0567, -0.0550, -0.0583, -0.0600, -0.0500, -0.0500, -0.0317, -0.0317, 0.0150, 0.0350, -0.1067, -0.1067, -0.0650, -0.0633, -0.0283, -0.0267, -0.0167, -0.0183, -0.0117, -0.0117, -0.0050... |
EMG | You are a time series analysis expert. This is a short-duration electromyography (EMG) signal recorded using a concentric needle electrode inserted into the tibialis anterior muscle: <ts><ts/>. The recording captures the amplitude (mV) of electrical activity generated by muscle fibers innervated by motor neurons. This... | Healthy | <THINK>**Step 1. Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires analyzing EMG signal amplitude values to classify the neuromuscular condition into one of three predefined labels (Healthy, Myopathy, Neuropathy). The core objective is to infer the condition based... | This is a classification task. | Minimum amplitude; maximum amplitude; trend; fluctuation; continuity. | A) healthy | A) healthy | -0.0483, -0.0433, -0.0433, -0.0367, -0.0367, -0.0283, -0.0283, -0.0317, -0.0300, -0.0267, -0.0283, -0.0350, -0.0350, -0.0433, -0.0450, -0.0483, -0.0483, -0.0550, -0.0567, -0.0650, -0.0650, -0.0733, -0.0733, -0.0783, -0.0767, -0.0733, -0.0733, -0.0683, -0.0683, -0.0650, -0.0667, -0.0650, -0.0650, -0.0683, -0.0683, -0.07... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | atrial fibrillation | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying a single-lead ECG time series into one of four predefined rhythm categories (normal sinus rhythm, atrial fibrillation, other cardiac rhythms, or noise). The core objective is patter... | This is a classification task. | P-wave presence/absence; qrs complex regularity; rr interval variability; baseline noise level; waveform morphology consistency; trend; amplitude; fluctuation; continuity; judgment criteria; threshold values | B) atrial fibrillation | B) atrial fibrillation | 0.3740, 0.3780, 0.2450, 0.0910, 0.0280, 0.0120, 0.0020, -0.0050, -0.0130, -0.0220, -0.0280, -0.0320, -0.0350, -0.0370, -0.0400, -0.0430, -0.0460, -0.0490, -0.0520, -0.0540, -0.0590, -0.0660, -0.0810, -0.0940, -0.1080, -0.1270, -0.1420, -0.1500, -0.1550, -0.1580, -0.1590, -0.1580, -0.1540, -0.1480, -0.1400, -0.1230, -0.... |
EMG | You are a time series analysis expert. This is a short-duration electromyography (EMG) signal recorded using a concentric needle electrode inserted into the tibialis anterior muscle: <ts><ts/>. The recording captures the amplitude (mV) of electrical activity generated by muscle fibers innervated by motor neurons. This... | Neuropathy | <THINK>**Step 1. Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires analyzing EMG signal amplitude values to classify it into one of three neuromuscular conditions (Healthy, Myopathy, Neuropathy). Specific keywords include "analyze the approximate minimum and maxim... | This is a classification task. | Amplitude range; amplitude distribution; trend; fluctuation; continuity; judgment criteria; threshold values | C)neuropathy | C)neuropathy | 0.0000, -0.0017, 0.0483, 0.0517, 0.1400, 0.1433, 0.2467, 0.2500, 0.3733, 0.3800, 0.5500, 0.5683, 0.2917, 0.2617, -0.5600, -0.7600, -0.1350, -0.5017, 0.7133, 0.7350, -2.4117, -2.4283, -1.1183, -1.0417, 0.1250, 0.1400, 0.4150, 0.4367, 0.4117, 0.4083, 0.3133, 0.3100, 0.2400, 0.2383, 0.1433, 0.1417, 0.1067, 0.1050, 0.0917,... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | other cardiac rhythms | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem explicitly requires classifying a single-lead ECG time series into one of four predefined rhythm categories: "normal sinus rhythm," "atrial fibrillation," "other cardiac rhythms," or "noise." The core o... | This is a classification task. | Regularity of r-r intervals; presence/absence of p waves; qrs complex morphology; signal-to-noise ratio; trend; fluctuation; amplitude; continuity. | C) other cardiac rhythms | C) other cardiac rhythms | -0.0360, -0.0260, 0.0240, 0.1900, 0.4410, 0.7000, 0.8000, 0.6510, 0.2310, -0.1380, -0.2110, -0.1080, -0.0710, -0.0640, -0.0590, -0.0560, -0.0540, -0.0520, -0.0510, -0.0510, -0.0490, -0.0470, -0.0430, -0.0390, -0.0290, -0.0120, 0.0050, 0.0190, 0.0300, 0.0380, 0.0500, 0.0670, 0.0870, 0.0980, 0.1030, 0.1070, 0.1070, 0.098... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | atrial fibrillation | <THINK>**Step 1. Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying a single-lead ECG time series into one of four predefined rhythm categories (normal sinus rhythm, atrial fibrillation, other cardiac rhythms, or noise). The core objective is pattern re... | This is a classification task. | Rhythm regularity; p wave presence/morphology; qrs complex characteristics; signal-to-noise ratio; trend; amplitude; fluctuation; continuity. | B) atrial fibrillation | B) atrial fibrillation | 0.0280, 0.0260, 0.0230, 0.0190, 0.0140, 0.0090, 0.0040, -0.0010, -0.0160, -0.0410, -0.0600, -0.0740, -0.0840, -0.0950, -0.1130, -0.1410, -0.1800, -0.2230, -0.2480, -0.2610, -0.2690, -0.2750, -0.2820, -0.2860, -0.2760, -0.2470, -0.1880, -0.1290, -0.0720, -0.0180, 0.0230, 0.0500, 0.0590, 0.0640, 0.0700, 0.0730, 0.0750, 0... |
RCW | You are analyzing an audio signal to determine the presence of right whale vocalizations.
Up-calls are the most commonly documented right whale vocalisation with anacoustic signature of approximately 60Hz-250Hz, typically lasting 1second.
Right whale calls can often be difficult to hear as the lowfrequency band can b... | No Right Whale | <think>Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires determining whether a specific acoustic signature (60Hz-250Hz, ~1 second) exists in a given 2-second audio waveform. The core objective is binary classification: deciding if the signal contains a r... | This is a classification task. | Frequency range; duration; signal-to-noise ratio; trend; amplitude; fluctuation; continuity | B)no right whale. | B)no right whale | 0.1273, 0.0656, 0.0388, -0.2411, 0.1474, -0.0525, -0.1245, 0.0937, -0.0107, 0.0540, -0.0562, 0.0925, -0.0476, 0.0693, 0.0165, 0.0381, -0.0427, -0.0632, -0.0473, -0.0687, 0.0324, 0.1090, 0.0214, 0.0125, -0.0726, -0.1181, 0.0613, -0.0058, -0.0314, 0.0375, 0.1065, -0.0415, -0.0204, -0.0977, 0.0839, 0.0281, -0.0879, -0.008... |
CTU | You are a time series analysis expert. This is a time series signal derived from electricity usage patterns in UK households, recorded as part of the government-sponsored study "Powering the Nation": <ts><ts/>. The signal represents energy consumption sampled every 2 minutes over a 24-hour period, resulting in a series... | Desktop | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying a time series of electricity usage into one of two predefined categories ("Desktop" or "Laptop") based on device-specific consumption patterns. Keywords like "classify the household’s... | This is a classification task. | Baseline consumption level; amplitude of consumption spikes; fluctuation frequency; duration of high-consumption states; trend; continuity | A)desktop | A)desktop | -0.3791, -0.3791, -0.3791, -0.3791, -0.2300, -0.3791, -0.3791, -0.3791, -0.3791, -0.2300, -0.3791, -0.3791, -0.3791, -0.3791, -0.3791, -0.3791, -0.3791, -0.3791, -0.3791, -0.2300, -0.3791, -0.3791, -0.3791, -0.3791, -0.2300, -0.3791, -0.3791, -0.3791, -0.3791, -0.3791, -0.3791, -0.3791, -0.3791, -0.3791, -0.2300, -0.37... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | atrial fibrillation | <THINK>**Step 1. Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem explicitly requires classifying the ECG time series into one of four predefined rhythm categories ("normal sinus rhythm," "atrial fibrillation," "other cardiac rhythms," or "noise"). Keywords like "classify... | This is a classification task. | P wave presence/absence; qrs complex regularity; rr interval variability; signal-to-noise ratio; trend; amplitude; fluctuation; continuity | B) atrial fibrillation | B) atrial fibrillation | -0.0370, -0.0260, -0.0130, 0.0030, 0.0200, 0.0320, 0.0370, 0.0380, 0.0380, 0.0400, 0.0350, 0.0100, -0.0110, -0.0200, -0.0260, -0.0190, 0.0000, 0.0180, 0.0310, 0.0410, 0.0480, 0.0410, 0.0260, 0.0050, -0.0110, -0.0210, 0.0030, 0.0180, 0.0300, 0.0380, 0.0440, 0.0490, 0.0490, 0.0410, 0.0290, 0.0120, 0.0020, 0.0030, 0.0070,... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | atrial fibrillation | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem explicitly requires classifying an ECG time series into one of four predefined rhythm categories ("normal sinus rhythm," "atrial fibrillation," "other cardiac rhythms," or "noise"). The background invol... | This is a classification task. | P wave presence/absence; qrs complex regularity; r-r interval consistency; signal noise level; trend; amplitude; fluctuation; continuity | B) atrial fibrillation | B) atrial fibrillation | -0.0580, -0.0580, -0.0580, -0.0590, -0.0600, -0.0600, -0.0620, -0.0650, -0.0660, -0.0680, -0.0710, -0.0750, -0.0800, -0.0840, -0.0850, -0.0850, -0.0820, -0.0790, -0.0730, -0.0650, -0.0570, -0.0440, 0.0270, 0.1770, 0.4080, 0.6240, 0.5750, 0.1360, -0.4460, -0.7300, -0.6820, -0.5180, -0.3800, -0.2630, -0.1360, -0.0460, -0... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | other cardiac rhythms | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying a single-lead ECG time series into one of four predefined rhythm categories (normal sinus rhythm, atrial fibrillation, other cardiac rhythms, or noise). Key phrases like "classify t... | This is a classification task. | P wave presence/consistency; qrs complex morphology/regularity; rr interval variability; signal-to-noise ratio; trend; amplitude; fluctuation; continuity; judgment criteria; threshold values. | C) other cardiac rhythms. | C) other cardiac rhythms | -0.0210, -0.0460, -0.0750, -0.0950, -0.0860, -0.0660, -0.0570, -0.0510, -0.0430, -0.0330, -0.0260, -0.0180, -0.0040, 0.0520, 0.0890, 0.1540, 0.2880, 0.4630, 0.6350, 0.7560, 0.7000, 0.4710, 0.2110, -0.0310, -0.1930, -0.2350, -0.2480, -0.2470, -0.2310, -0.1870, -0.1410, -0.1080, -0.0940, -0.0850, -0.0770, -0.0720, -0.067... |
RCW | You are analyzing an audio signal to determine the presence of right whale vocalizations.
Up-calls are the most commonly documented right whale vocalisation with anacoustic signature of approximately 60Hz-250Hz, typically lasting 1second.
Right whale calls can often be difficult to hear as the lowfrequency band can b... | Right Whale Present | <think>**Step 1. Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires determining whether a 2-second audio waveform contains a right whale vocalization (specifically an up-call), with predefined criteria: frequency range (60–250 Hz) and duration (~1 second). The core... | This is a classification task. | Frequency band concentration; duration of sustained energy; signal-to-noise ratio in low frequencies; amplitude modulation characteristics; trend; fluctuation; continuity; threshold values; judgment criteria. | A) right whale present | A) right whale present | 3.1503, 2.6614, 1.3425, 0.0806, 0.4471, -0.2841, 0.0674, 0.6116, 1.2540, 0.4309, 0.1386, 1.0763, -0.2002, -0.0842, -0.4797, -0.9256, -0.8524, -0.2875, -0.1263, -0.0632, 0.1224, -0.1260, -0.3174, 0.0143, -0.0687, 0.0626, 0.0406, 0.0711, -0.3189, 0.0784, 0.2084, -0.1431, 0.2628, 0.2133, -0.8359, -0.8020, 0.7611, 1.5057, ... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | other cardiac rhythms | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem explicitly requires classifying the ECG time series into one of four predefined rhythm categories ("normal sinus rhythm," "atrial fibrillation," "other cardiac rhythms," or "noise"). The core objective is... | This is a classification task. | P-wave presence/consistency; qrs complex regularity; rr interval variability; signal-to-noise ratio; trend; amplitude; fluctuation; continuity. | C) other cardiac rhythms. | C) other cardiac rhythms | -0.1570, -0.1190, -0.0870, -0.0570, 0.0000, 0.0660, 0.0910, 0.1140, 0.1540, 0.1860, 0.2170, 0.2260, 0.2280, 0.2150, 0.2030, 0.1950, 0.2030, 0.2170, 0.2370, 0.2560, 0.2690, 0.2790, 0.2860, 0.2980, 0.3170, 0.3380, 0.3530, 0.3860, 0.4180, 0.4380, 0.4520, 0.4650, 0.4750, 0.4860, 0.5010, 0.5180, 0.5260, 0.5120, 0.4880, 0.45... |
EMG | You are a time series analysis expert. This is a short-duration electromyography (EMG) signal recorded using a concentric needle electrode inserted into the tibialis anterior muscle: <ts><ts/>. The recording captures the amplitude (mV) of electrical activity generated by muscle fibers innervated by motor neurons. This... | Neuropathy | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires analyzing EMG signal amplitudes to classify the neuromuscular condition into one of three discrete labels (Healthy, Myopathy, Neuropathy). The core objective is to infer the pathology based on ... | This is a classification task. | Minimum amplitude; maximum amplitude; trend; fluctuation; continuity. | C)neuropathy | C)neuropathy | 0.0350, 0.1083, 0.1117, 0.1917, 0.1967, 0.3050, 0.3117, 0.4733, 0.4800, 0.5667, 0.4717, -0.2350, -0.2683, -0.7700, -0.7050, 0.0650, 1.0867, -0.3850, -0.7817, -2.3783, -1.7200, -0.0467, -0.0067, 0.4083, 0.4167, 0.4783, 0.4350, 0.2200, 0.2117, 0.0250, 0.0200, -0.0450, -0.0650, -0.0450, -0.0450, -0.0017, 0.0017, 0.0400, 0... |
RCW | You are analyzing an audio signal to determine the presence of right whale vocalizations.
Up-calls are the most commonly documented right whale vocalisation with anacoustic signature of approximately 60Hz-250Hz, typically lasting 1second.
Right whale calls can often be difficult to hear as the lowfrequency band can b... | Right Whale Present | <think>Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The core objective is to determine whether a specific biological signal (right whale up-call) exists in a given audio waveform. The problem defines the acoustic signature (60–250 Hz, ~1s duration) and requires a binary... | This is a classification task. | Frequency range; duration; amplitude threshold; anthropogenic noise interference; trend; fluctuation; continuity. | A) right whale present. | A) right whale present | 0.0937, -0.0238, -0.0922, 0.0076, 0.0305, 0.0775, -0.0226, -0.0751, -0.0241, -0.0400, -0.0430, 0.0537, -0.0815, -0.0296, 0.0781, 0.1700, 0.0409, 0.0430, 0.0800, 0.0381, 0.0287, 0.0299, 0.0403, 0.0009, -0.0140, 0.0177, 0.0281, 0.0256, 0.0384, -0.0360, -0.1273, -0.0186, 0.1416, -0.0220, 0.0263, -0.0680, -0.0073, 0.0131, ... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | normal sinus rhythm | <THINK>**Step 1. Analyzing task intent:**
[Judgment] This is a classification task.
[Description] The problem requires classifying a single-lead ECG time series into one of four predefined rhythm categories (normal sinus rhythm, atrial fibrillation, other cardiac rhythms, or noise). Keywords like "classify the hear... | This is a classification task. | Regularity of rr intervals; presence/consistency of p waves; qrs morphology stability; signal-to-noise ratio; trend; amplitude; fluctuation; continuity. | A) normal sinus rhythm | A) normal sinus rhythm | 0.4140, 0.4240, 0.4310, 0.4410, 0.4580, 0.4780, 0.5040, 0.5280, 0.5510, 0.5740, 0.6020, 0.6360, 0.6700, 0.7170, 0.7610, 0.8080, 0.8600, 0.9130, 0.9650, 1.0170, 1.0600, 1.0870, 1.1050, 1.1050, 1.0840, 1.0350, 0.9310, 0.7780, 0.5820, 0.3770, 0.1880, 0.0270, -0.1070, -0.2110, -0.2850, -0.3400, -0.3730, -0.3940, -0.4020, -... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | normal sinus rhythm | <THINK>**Step 1. Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying a single-lead ECG time series into one of four predefined rhythm categories: 'normal sinus rhythm', 'atrial fibrillation', 'other cardiac rhythms', or 'noise'. The core objective is to ... | This is a classification task. | P-wave presence/consistency; rr interval regularity; qrs complex morphology; baseline noise level; trend; amplitude; fluctuation; continuity. | A) normal sinus rhythm | A) normal sinus rhythm | 0.2750, 0.2830, 0.2850, 0.2750, 0.2560, 0.2290, 0.2090, 0.1890, 0.1430, 0.1100, 0.0790, 0.0320, 0.0000, -0.0250, -0.0600, -0.0840, -0.1000, -0.1160, -0.1230, -0.1300, -0.1340, -0.1370, -0.1400, -0.1420, -0.1420, -0.1410, -0.1390, -0.1350, -0.1300, -0.1250, -0.1160, -0.0980, -0.0750, -0.0650, -0.0590, -0.0550, -0.0510, ... |
CTU | You are a time series analysis expert. This is a time series signal derived from electricity usage patterns in UK households, recorded as part of the government-sponsored study "Powering the Nation": <ts><ts/>. The signal represents energy consumption sampled every 2 minutes over a 24-hour period, resulting in a series... | Laptop | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem explicitly requires classifying the electricity usage pattern into one of two predefined labels ("Desktop" or "Laptop") based on energy consumption data. Keywords such as "classify the household’s devic... | This is a classification task. | Peak amplitude; fluctuation frequency; duration of high-consumption states; baseline stability; trend; continuity. | B) laptop | B) laptop | -0.2297, -0.2297, 0.0883, -0.2297, -0.2297, -0.2297, -0.2297, -0.2297, -0.2297, -0.2297, -0.2297, -0.2297, -0.2297, -0.2297, -0.2297, -0.2297, -0.2297, -0.2297, -0.2297, -0.2297, -0.2297, -0.2297, -0.2297, -0.2297, -0.2297, -0.2297, -0.2297, -0.2297, -0.2297, -0.2297, -0.2297, -0.2297, -0.2297, -0.2297, -0.2297, -0.229... |
RCW | You are analyzing an audio signal to determine the presence of right whale vocalizations.
Up-calls are the most commonly documented right whale vocalisation with anacoustic signature of approximately 60Hz-250Hz, typically lasting 1second.
Right whale calls can often be difficult to hear as the lowfrequency band can b... | Right Whale Present | <think>Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires determining whether a specific acoustic signal (a 2-second waveform) contains a right whale vocalization. The core objective is binary classification: "Right Whale Present" or "No Right Whale." Key... | This is a classification task. | Frequency band; duration; amplitude consistency; anthropogenic noise interference; trend; fluctuation; continuity. | A) right whale present | A) right whale present | 0.0275, 0.0326, 0.0146, -0.1782, -0.0507, 0.1553, -0.0146, 0.0827, -0.1767, -0.2371, 0.1495, 0.1083, 0.2588, 0.2576, 0.3015, 0.1038, 0.4700, -0.0107, -0.0394, -0.0989, -0.0003, -0.0736, 0.0226, -0.1614, 0.0598, 0.3458, 0.2106, -0.0931, 0.1569, -0.0244, 0.0763, 0.2634, -0.0229, 0.0720, -0.1077, 0.0006, 0.0558, 0.0265, 0... |
EMG | You are a time series analysis expert. This is a short-duration electromyography (EMG) signal recorded using a concentric needle electrode inserted into the tibialis anterior muscle: <ts><ts/>. The recording captures the amplitude (mV) of electrical activity generated by muscle fibers innervated by motor neurons. This... | Neuropathy | <THINK>Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires analyzing an EMG signal's amplitude characteristics (minimum and maximum values) and assigning it to one of three predefined neuromuscular condition labels (Healthy, Myopathy, Neuropathy). Keywords li... | This is a classification task. | Minimum amplitude; maximum amplitude; overall amplitude magnitude; trend; fluctuation; continuity; threshold values. | C)neuropathy. | C)neuropathy | 0.0567, 0.0633, 0.0600, 0.0533, 0.0550, 0.0500, 0.0483, 0.0583, 0.0583, 0.0550, 0.0583, 0.0450, 0.0450, 0.0283, 0.0267, 0.0233, 0.0233, 0.0167, 0.0150, 0.0050, 0.0033, 0.0000, 0.0000, -0.0217, -0.0233, -0.0650, -0.0767, -0.0650, -0.0633, 0.0333, 0.0367, 0.1550, 0.1600, 0.2950, 0.3017, 0.4583, 0.4667, 0.6117, 0.5967, 0.... |
EMG | You are a time series analysis expert. This is a short-duration electromyography (EMG) signal recorded using a concentric needle electrode inserted into the tibialis anterior muscle: <ts><ts/>. The recording captures the amplitude (mV) of electrical activity generated by muscle fibers innervated by motor neurons. This... | Neuropathy | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires labeling an EMG signal as "Healthy," "Myopathy," or "Neuropathy" based on amplitude analysis. The core objective is to classify neuromuscular conditions using amplitude characteristics (explici... | This is a classification task. | Minimum amplitude; maximum amplitude; trend; fluctuation; continuity. | C)neuropathy. | C)neuropathy | -0.0050, -0.0067, -0.0233, -0.0233, -0.0500, -0.0517, -0.0900, -0.0917, -0.1267, -0.1283, -0.1567, -0.1600, -0.1883, -0.1900, -0.2000, -0.2000, -0.1783, -0.1783, -0.1217, -0.1200, -0.0767, -0.0750, -0.0600, -0.0583, -0.0433, -0.0433, -0.0383, -0.0433, -0.0317, -0.0300, 0.0050, 0.0067, 0.0333, 0.0333, 0.0650, 0.1000, 0.... |
RCW | You are analyzing an audio signal to determine the presence of right whale vocalizations.
Up-calls are the most commonly documented right whale vocalisation with anacoustic signature of approximately 60Hz-250Hz, typically lasting 1second.
Right whale calls can often be difficult to hear as the lowfrequency band can b... | Right Whale Present | <think>**Step 1 Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem explicitly requires determining the presence/absence of a right whale vocalization in a given audio signal, with two discrete output choices ("Right Whale Present" or "No Right Whale"). The core objective is... | This is a classification task. | Frequency range; duration; amplitude profile; noise interference; trend; fluctuation; continuity. | A) right whale present. | A) right whale present | 0.0522, -0.2048, -0.1202, -0.1526, 0.0180, -0.0693, -0.1926, 0.0934, 0.0024, -0.0247, -0.0497, 0.0745, 0.0351, -0.0784, -0.0592, -0.0079, 0.0641, 0.1352, 0.0790, 0.0214, -0.0092, 0.0977, -0.0873, 0.0174, 0.0757, 0.0259, 0.1062, -0.0122, 0.1450, -0.0052, 0.0104, 0.0037, -0.0247, 0.0580, 0.0476, -0.0220, -0.0156, 0.0238,... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | other cardiac rhythms | <THINK>Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying a single-lead ECG time series into one of four predefined rhythm categories (normal sinus rhythm, atrial fibrillation, other cardiac rhythms, or noise). Keywords like "classify the hear... | This is a classification task. | Rhythm regularity; p wave presence; qrs complex morphology; noise level; trend; amplitude; fluctuation; continuity. | C) other cardiac rhythms. | C) other cardiac rhythms | -0.4660, -0.4410, -0.4160, -0.3880, -0.3610, -0.3340, -0.3130, -0.2950, -0.2790, -0.2650, -0.2510, -0.2310, -0.2060, -0.1800, -0.1530, -0.1320, -0.1170, -0.1050, -0.0980, -0.0940, -0.0890, -0.0840, -0.0790, -0.0740, -0.0670, -0.0590, -0.0460, -0.0330, -0.0220, -0.0120, -0.0020, 0.0050, 0.0120, 0.0180, 0.0260, 0.0350, 0... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | other cardiac rhythms | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying a single-lead ECG time series into one of four predefined rhythm categories: "normal sinus rhythm," "atrial fibrillation," "other cardiac rhythms," or "noise." The core objective is... | This is a classification task. | P wave presence; qrs complex consistency; rr interval regularity; noise level; trend; amplitude; fluctuation; continuity. | C) other cardiac rhythms. | C) other cardiac rhythms | 0.0700, 0.0760, 0.0930, 0.1630, 0.3070, 0.5190, 0.7780, 0.9430, 0.8650, 0.5260, 0.1960, 0.0800, 0.0720, 0.0700, 0.0740, 0.0770, 0.0800, 0.0820, 0.0840, 0.0840, 0.0830, 0.0800, 0.0750, 0.0700, 0.0670, 0.0650, 0.0630, 0.0620, 0.0610, 0.0620, 0.0650, 0.0680, 0.0690, 0.0680, 0.0670, 0.0650, 0.0630, 0.0600, 0.0560, 0.0540, ... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | normal sinus rhythm | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying a single-lead ECG time series into one of four predefined rhythm categories (normal sinus rhythm, atrial fibrillation, other cardiac rhythms, or noise). Key phrases like "classify t... | This is a classification task. | P-wave presence/consistency; qrs complex regularity; rr interval variability; signal-to-noise ratio; trend; amplitude; fluctuation; continuity; judgment criteria; threshold values. | A) normal sinus rhythm. | A) normal sinus rhythm | -0.2840, -0.2860, -0.2820, -0.2710, -0.2600, -0.2500, -0.2420, -0.2350, -0.2280, -0.2220, -0.2160, -0.2100, -0.2040, -0.1980, -0.1900, -0.1810, -0.1680, -0.1490, -0.1370, -0.1290, -0.1230, -0.1180, -0.1190, -0.1290, -0.1470, -0.1870, -0.2270, -0.2680, -0.3080, -0.3340, -0.3440, -0.3500, -0.3530, -0.3520, -0.3460, -0.33... |
EMG | You are a time series analysis expert. This is a short-duration electromyography (EMG) signal recorded using a concentric needle electrode inserted into the tibialis anterior muscle: <ts><ts/>. The recording captures the amplitude (mV) of electrical activity generated by muscle fibers innervated by motor neurons. This... | Neuropathy | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires analyzing an EMG signal's amplitude values to classify it into one of three neuromuscular conditions (Healthy, Myopathy, Neuropathy). Key phrases like "choose the best matching label" and the pre... | This is a classification task. | Minimum amplitude; maximum amplitude; trend; fluctuation; continuity. | C)neuropathy. | C)neuropathy | -0.0600, -0.0600, -0.0417, -0.0417, -0.0267, -0.0250, -0.0100, -0.0083, 0.0050, 0.0067, 0.0133, 0.0117, 0.0183, 0.0183, 0.0250, 0.0267, 0.0217, 0.0250, 0.0300, 0.0283, 0.0200, 0.0200, 0.0050, 0.0033, -0.0033, -0.0017, -0.0050, -0.0033, 0.0000, 0.0017, 0.0067, 0.0067, 0.0167, 0.0117, 0.0167, 0.0150, 0.0117, 0.0117, 0.02... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | noise | <THINK>**Step 1. Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem explicitly requires classifying the ECG time series into one of four predefined rhythm categories ("normal sinus rhythm", "atrial fibrillation", "other cardiac rhythms", or "noise"). Keywords like "classify... | This is a classification task. | P-wave presence/regularity; qrs complex regularity; rr interval variability; signal-to-noise ratio; trend; amplitude; fluctuation; continuity; judgment criteria; threshold values. | D) noise. | D) noise | 0.0180, 0.2070, -0.0500, 0.2860, 0.0570, 0.1120, 0.0490, -0.1140, 0.0930, -0.2060, -0.0580, 0.0970, -0.1050, 0.2650, 0.0190, 0.2120, 0.2710, 0.1900, 0.2810, 0.0500, 0.0740, 0.1770, 0.1270, -0.2650, -0.9300, -1.1510, -0.4900, 0.3630, -0.2650, 0.0020, 0.1640, 0.0620, 0.1090, -0.1830, -0.2020, -0.2050, -0.1190, -0.1590, -... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | atrial fibrillation | <THINK>**Step 1. Analyzing task intent:**
[Judgment] This is a classification task.
[Description] The problem requires classifying a single-lead ECG time series into one of four predefined rhythm categories: 'normal sinus rhythm', 'atrial fibrillation', 'other cardiac rhythms', or 'noise'. The core objective is to ... | This is a classification task. | Qrs complex regularity; p wave presence/consistency; baseline stability; rhythm irregularity; signal-to-noise ratio; trend; amplitude; fluctuation; continuity; judgment criteria; threshold values. | B) atrial fibrillation | B) atrial fibrillation | -0.3270, -0.2770, -0.2040, -0.1170, -0.0440, 0.0000, 0.0030, 0.0030, 0.0040, 0.0040, 0.0040, 0.0070, 0.0110, 0.0150, 0.0200, 0.0290, 0.0400, 0.0530, 0.0640, 0.0740, 0.0820, 0.0880, 0.0950, 0.1100, 0.1320, 0.1480, 0.1580, 0.1640, 0.1700, 0.1770, 0.1760, 0.1610, 0.1340, 0.0940, 0.0540, 0.0260, -0.0040, -0.0320, -0.0600, ... |
EMG | You are a time series analysis expert. This is a short-duration electromyography (EMG) signal recorded using a concentric needle electrode inserted into the tibialis anterior muscle: <ts><ts/>. The recording captures the amplitude (mV) of electrical activity generated by muscle fibers innervated by motor neurons. This... | Neuropathy | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem involves classifying an EMG signal into one of three neuromuscular conditions (Healthy, Myopathy, or Neuropathy) based on the analysis of approximate minimum and maximum amplitude values. The core objecti... | This is a classification task. | Minimum amplitude; maximum amplitude; trend; fluctuation; continuity | C)neuropathy | C)neuropathy | 0.0233, 0.0183, 0.0217, 0.0417, 0.0417, 0.0483, 0.0467, 0.0417, 0.0417, 0.0467, 0.0433, 0.0350, 0.0333, -0.0100, -0.0133, -0.1417, -0.1483, -0.2483, -0.2533, -0.2967, -0.2883, -0.1633, -0.1583, -0.0167, -0.0100, 0.1783, 0.1883, 0.3617, 0.3883, 0.2683, 0.2550, -0.2267, -0.2533, -0.7267, -0.7617, -0.5750, -0.5583, -0.143... |
EMG | You are a time series analysis expert. This is a short-duration electromyography (EMG) signal recorded using a concentric needle electrode inserted into the tibialis anterior muscle: <ts><ts/>. The recording captures the amplitude (mV) of electrical activity generated by muscle fibers innervated by motor neurons. This... | Myopathy | <THINK>**Step 1. Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires labeling the EMG signal as "Healthy," "Myopathy," or "Neuropathy" based on amplitude characteristics. Key requirements include: (1) analyzing "amplitude (mV) of electrical activity," (2) identifyi... | This is a classification task. | Minimum amplitude; maximum amplitude; trend; fluctuation; continuity | B)myopathy | B)myopathy | -0.0050, 0.0217, 0.0183, 0.0233, 0.0233, 0.0183, 0.0217, -0.0367, -0.0500, -0.0167, -0.0167, -0.0033, -0.0050, 0.0000, -0.0033, 0.0050, 0.0017, 0.0067, 0.0050, 0.0017, 0.0033, 0.0067, 0.0033, 0.0067, 0.0033, 0.0083, 0.0050, 0.0083, 0.0067, 0.0100, 0.0083, 0.0017, 0.0050, 0.0000, 0.0050, 0.0000, 0.0017, 0.0050, -0.0017,... |
EMG | You are a time series analysis expert. This is a short-duration electromyography (EMG) signal recorded using a concentric needle electrode inserted into the tibialis anterior muscle: <ts><ts/>. The recording captures the amplitude (mV) of electrical activity generated by muscle fibers innervated by motor neurons. This... | Neuropathy | <THINK>Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires labeling the EMG signal as "Healthy," "Myopathy," or "Neuropathy" based on amplitude characteristics. The core objective is neuromuscular condition diagnosis, explicitly stated through keywords: "... | This is a classification task. | Amplitude range; sustained high-amplitude spikes; trend; fluctuation; continuity. | C)neuropathy. | C)neuropathy | 0.4450, 0.5600, 0.5233, -0.1100, -0.1483, -0.8133, -0.8117, 0.0217, -0.2867, 0.8417, -0.0633, -2.4150, -1.9733, -0.3450, -0.2867, 0.2800, 0.2950, 0.4583, 0.4567, 0.3900, 0.3867, 0.3033, 0.2983, 0.2183, 0.2167, 0.1283, 0.1250, 0.0633, 0.0600, 0.0367, 0.0350, 0.0133, 0.0133, -0.0067, -0.0083, -0.0183, -0.0183, -0.0367, -... |
CTU | You are a time series analysis expert. This is a time series signal derived from electricity usage patterns in UK households, recorded as part of the government-sponsored study "Powering the Nation": <ts><ts/>. The signal represents energy consumption sampled every 2 minutes over a 24-hour period, resulting in a series... | Desktop | <THINK>Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem explicitly requires classifying the electricity usage pattern into one of two predefined classes ("Desktop" or "Laptop") based on the provided time series signal. The core objective is to infer the device type... | This is a classification task. | Sustained high consumption periods; variability (fluctuation range); presence of charging spikes; baseline stability; trend; amplitude; continuity. | A)desktop | A)desktop | -1.3639, -1.2537, -1.2537, -1.3639, -1.2537, -1.3639, -1.2537, -1.2537, -1.3639, -1.2537, -1.3639, -1.2537, -1.2537, -1.3639, -1.2537, -1.2537, -1.3639, -1.2537, -1.3639, -1.2537, -1.2537, -1.3639, -1.2537, -1.2537, -1.3639, -1.2537, -1.3639, -1.2537, -1.2537, -1.3639, -1.2537, -1.3639, -1.2537, -1.2537, -1.3639, -1.25... |
EMG | You are a time series analysis expert. This is a short-duration electromyography (EMG) signal recorded using a concentric needle electrode inserted into the tibialis anterior muscle: <ts><ts/>. The recording captures the amplitude (mV) of electrical activity generated by muscle fibers innervated by motor neurons. This... | Neuropathy | <THINK>**Step 1. Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying an EMG signal into one of three predefined neuromuscular conditions (Healthy, Myopathy, Neuropathy) based on its amplitude characteristics. The core objective is to infer the condition... | This is a classification task. | Amplitude range; amplitude distribution characteristics; trend; fluctuation; continuity; judgment criteria; threshold values | C)neuropathy. | C)neuropathy | -0.0183, -0.0450, -0.0433, 0.0033, 0.0050, -0.1600, -0.1800, -0.0633, -0.0483, 0.3483, 0.3650, 0.7317, 0.7467, 0.9950, 1.0033, 1.1100, 1.1150, 0.7217, 0.6667, -1.3767, -1.5917, -0.8717, -0.9733, -2.7033, -1.6733, -1.0267, -1.0033, -0.3933, -0.3633, 0.2200, 0.2367, 0.6117, 0.6217, 0.7417, 0.7417, 0.5267, 0.5133, 0.1333,... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | noise | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying a single-lead ECG time series into one of four predefined rhythm categories ("normal sinus rhythm," "atrial fibrillation," "other cardiac rhythms," or "noise"). The core objective i... | This is a classification task. | Rhythm regularity; p wave presence; qrs complex consistency; signal-to-noise ratio; trend; amplitude; fluctuation; continuity; judgment criteria; threshold values. | D) noise. | D) noise | 0.0060, -0.0010, -0.0060, -0.0050, 0.0010, 0.0110, 0.0430, 0.1140, 0.1360, 0.1280, 0.0430, -0.0200, -0.0390, -0.0480, -0.0540, -0.0600, -0.0660, -0.0700, -0.0730, -0.0750, -0.0780, -0.0800, -0.0820, -0.0830, -0.0840, -0.0840, -0.0850, -0.0850, -0.0840, -0.0840, -0.0830, -0.0840, -0.0840, -0.0850, -0.0850, -0.0840, -0.0... |
CTU | You are a time series analysis expert. This is a time series signal derived from electricity usage patterns in UK households, recorded as part of the government-sponsored study "Powering the Nation": <ts><ts/>. The signal represents energy consumption sampled every 2 minutes over a 24-hour period, resulting in a series... | Laptop | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires assigning the given electricity usage time series to one of two predefined classes ("Desktop" or "Laptop") based on device-specific energy consumption patterns. Keywords like "classify the hous... | This is a classification task. | Baseline energy level; sustained high-power periods; peak magnitude and duration; periodicity of usage; trend; fluctuation; continuity. | B)laptop | B)laptop | 0.4975, -0.3453, 0.4975, -0.3453, 0.4975, -0.3453, 0.4975, -0.3453, 0.4975, -0.3453, -0.3453, 0.4975, -0.3453, 0.4975, -0.3453, 0.4975, -0.3453, 0.4975, -0.3453, 0.4975, -0.3453, 0.4975, -0.3453, 0.4975, -0.3453, -0.3453, 0.4975, -0.3453, 0.4975, -0.3453, 0.4975, -0.3453, 0.4975, -0.3453, 0.4975, -0.3453, 0.4975, -0.34... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | normal sinus rhythm | <THINK>### Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires classifying an ECG time series into one of four predefined rhythm categories (normal sinus rhythm, atrial fibrillation, other cardiac rhythms, or noise). Key phrases like "classify the heart rh... | This is a classification task. | Qrs complex regularity; p-wave presence/morphology; rr interval variability; signal-to-noise ratio; waveform amplitude stability; trend; fluctuation; continuity | A) normal sinus rhythm | A) normal sinus rhythm | -0.0700, 0.1230, 0.4290, 0.7520, 0.8530, 0.6430, 0.2330, -0.0650, -0.1530, -0.1530, -0.1160, -0.0650, -0.0460, -0.0570, -0.0770, -0.0930, -0.1070, -0.1090, -0.0990, -0.0830, -0.0640, -0.0520, -0.0510, -0.0510, -0.0540, -0.0560, -0.0580, -0.0590, -0.0560, -0.0480, -0.0180, 0.0230, 0.0660, 0.1010, 0.1300, 0.1580, 0.1970,... |
CTU | You are a time series analysis expert. This is a time series signal derived from electricity usage patterns in UK households, recorded as part of the government-sponsored study "Powering the Nation": <ts><ts/>. The signal represents energy consumption sampled every 2 minutes over a 24-hour period, resulting in a series... | Laptop | <THINK>**Step 1. Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem explicitly requires classifying a household's device usage pattern into one of two predefined classes ("Desktop" or "Laptop") based on energy consumption time series data. Keywords include "classify," "devi... | This is a classification task. | Peak magnitude and duration; baseline stability; variability; cyclic patterns; trend; amplitude; fluctuation; continuity. | B)laptop | B)laptop | -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.3309, -0.33... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | other cardiac rhythms | <THINK>**Step 1. Analyzing task intent:**
[Judgment] This is a classification task.
[Description] The problem explicitly requires classifying the ECG rhythm into one of four predefined categories ("normal sinus rhythm," "atrial fibrillation," "other cardiac rhythms," or "noise"). Keywords like "classify the heart r... | This is a classification task. | P-wave presence/consistency; qrs complex regularity; r-r interval variability; baseline noise level; trend; amplitude; fluctuation; continuity. | C) other cardiac rhythms. | C) other cardiac rhythms | 0.5080, 0.8230, 0.8910, 0.5950, 0.0190, -0.4110, -0.5340, -0.5700, -0.5870, -0.4540, -0.2280, -0.1050, -0.0560, -0.0200, 0.0190, 0.0570, 0.1060, 0.1230, 0.1290, 0.1230, 0.1070, 0.0650, 0.0320, -0.0040, -0.0450, -0.0610, -0.0430, -0.0100, 0.0180, 0.0320, 0.0470, 0.0650, 0.1160, 0.1700, 0.2210, 0.2390, 0.2460, 0.2530, 0.... |
CTU | You are a time series analysis expert. This is a time series signal derived from electricity usage patterns in UK households, recorded as part of the government-sponsored study "Powering the Nation": <ts><ts/>. The signal represents energy consumption sampled every 2 minutes over a 24-hour period, resulting in a series... | Desktop | <THINK>### Step 1. **Analyzing task intent**
[Judgment] This is a classification task.
[Description] The problem explicitly requires classifying the household’s device usage pattern into one of two predefined classes ("Desktop" or "Laptop") based on electricity consumption data. Keywords such as "classify," "device... | This is a classification task. | Consistency/variance; sustained high consumption; event patterns (peaks, plateaus); trend; amplitude; fluctuation; continuity. | A)desktop | A)desktop | 0.0325, -0.4881, -0.4881, -0.4881, 0.0325, -0.4881, -0.4881, -0.4881, 0.0325, -0.4881, -0.4881, -0.4881, 0.0325, -0.4881, -0.4881, -0.4881, -0.4881, 0.0325, -0.4881, -0.4881, -0.4881, 0.0325, -0.4881, -0.4881, -0.4881, 0.0325, -0.4881, -0.4881, -0.4881, 0.0325, -0.4881, -0.4881, -0.4881, 0.0325, 0.0325, -0.4881, -0.488... |
EMG | You are a time series analysis expert. This is a short-duration electromyography (EMG) signal recorded using a concentric needle electrode inserted into the tibialis anterior muscle: <ts><ts/>. The recording captures the amplitude (mV) of electrical activity generated by muscle fibers innervated by motor neurons. This... | Neuropathy | <THINK>Step 1. **Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem requires analyzing EMG signal amplitudes to classify it into one of three neuromuscular conditions (Healthy, Myopathy, Neuropathy). Specific keywords like "choose the best matching label" and the predefined... | This is a classification task. | Amplitude_min; amplitude_max; trend; fluctuation; continuity | C)neuropathy | C)neuropathy | 0.0367, 0.0350, 0.0300, 0.0300, 0.0250, 0.0267, 0.0217, 0.0217, 0.0067, 0.0067, -0.0133, -0.0133, -0.0333, -0.0333, -0.0483, -0.0467, -0.0517, -0.0517, -0.0317, -0.0317, -0.0183, -0.0183, -0.0083, -0.0083, -0.0033, -0.0033, -0.0083, -0.0050, -0.0100, -0.0133, -0.0067, -0.0050, 0.0050, 0.0033, 0.0067, 0.0033, -0.0083, -... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | noise | <THINK>**Step 1. Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem explicitly requires classifying the ECG time series into one of four predefined rhythm categories ("normal sinus rhythm," "atrial fibrillation," "other cardiac rhythms," or "noise"). Keywords like "classify... | This is a classification task. | Rhythm regularity; qrs complex morphology; p wave presence; signal noise characteristics; trend; amplitude; fluctuation; continuity. | D) noise. | D) noise | -0.3140, -0.3410, -0.3660, -0.3860, -0.3960, -0.3990, -0.4010, -0.3980, -0.3910, -0.3830, -0.3740, -0.3670, -0.3690, -0.3770, -0.4020, -0.4310, -0.4660, -0.4920, -0.4960, -0.4940, -0.4980, -0.4960, -0.4840, -0.4630, -0.4190, -0.3430, -0.2450, -0.1500, -0.0770, -0.0190, 0.0380, 0.0780, 0.0830, 0.0840, 0.0850, 0.0860, 0.... |
ECG | You are a time series analysis expert. This is a single-lead ECG signal collected using a smartphone-enabled device (AliveCor): <ts><ts/>. The time series data represent the electrical activity of the heart, sampled at 300 Hz and bandpass-filtered by the device. The sequence includes the full ECG waveform in the time d... | normal sinus rhythm | <THINK>**Step 1. Analyzing task intent**:
[Judgment] This is a classification task.
[Description] The problem explicitly requires classifying the heart rhythm pattern ("classify the heart rhythm pattern") into one of four predefined categories (normal sinus rhythm, atrial fibrillation, other cardiac rhythms, noise)... | This is a classification task. | P wave presence/consistency; qrs complex regularity; rr interval variability; signal-to-noise ratio; trend; amplitude; fluctuation; continuity; judgment criteria; threshold values. | A) normal sinus rhythm. | A) normal sinus rhythm | -0.0190, -0.0150, -0.0110, -0.0080, -0.0060, -0.0030, 0.0000, 0.0050, 0.0080, 0.0090, 0.0060, 0.0020, -0.0030, -0.0080, -0.0130, -0.0180, -0.0210, -0.0220, -0.0210, -0.0190, -0.0170, -0.0140, -0.0100, -0.0050, 0.0000, 0.0010, -0.0050, -0.0230, -0.0490, -0.0490, 0.0210, 0.2060, 0.5010, 0.8050, 0.8990, 0.7310, 0.3520, 0.... |
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