156 Hours of Labeled Power Consumption Dataset of Computer
Bibliographic record
Abstract
Overview This dataset contains the power consumption and ground truth for 40 runs of 4 activity hours on an intel NUC micro-PC. The power consumption is pre-processed from 10 KSPS to 20 SPS with a median filter. The ground truth contains the label for each sample with: -1=UNKNOWN, 0=OFF, 1=IDLE, 2=HIGH LOAD and 3=REBOOT. Usage To use the npy format, load the python package numpy and load the data with the `load` function: ```import numpy as np trace = np.load("trace_4.npy")gt = np.load("gt_12.npy")``` The CSV format can be loaded in a variety of software. Changelog An index issue was present in the first version. The ground truth and trace were offset by 1 (trace n was associated with ground truth n-1). Moreover, trace 2 did not have a corresponding ground truth. This new version fixes this issue and removes the trace without ground truth. Naming convention was also changed to include leading 0 and make listing easier in file explorers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".