{"id":"W6929867061","doi":"10.5281/zenodo.10698881","title":"156 Hours of Labeled Power Consumption Dataset of Computer","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Cognitive Abilities and Testing","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Ground truth; Power consumption; Python (programming language); Spurious relationship; Offset (computer science)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000455475,0.001901147,0.001201431,0.00169542,0.0005101733,0.0008342707,0.001433886,0.001027897,0.02284808],"category_scores_gemma":[0.002170007,0.0004152469,0.0008335603,0.002771182,0.000253882,0.0008061701,0.0007015971,0.0009973723,0.03056155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006865119,"about_ca_system_score_gemma":0.0006607657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009959694,"about_ca_topic_score_gemma":0.01811919,"domain_scores_codex":[0.9991633,0.00009893096,0.00006495871,0.0003326633,0.0002273216,0.0001128662],"domain_scores_gemma":[0.998769,0.0001751423,0.00007275551,0.000373815,0.0005131682,0.00009611681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005788208,0.00024282,0.006655421,0.0008551432,0.0001283221,0.0001151341,0.00005918042,0.005230964,0.001859766,0.0005457315,0.942472,0.0412566],"study_design_scores_gemma":[0.0004113469,0.0003640347,0.09570976,0.0003622358,0.0001531833,0.0004739457,0.0002426956,0.02779865,0.01019473,0.00432684,0.8597856,0.0001769152],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01651096,0.0007051002,0.004024303,0.0003068243,0.000274215,0.0001209237,0.9615559,0.009672945,0.006828785],"genre_scores_gemma":[0.01632123,0.0001550683,0.00237293,0.0001222311,0.00005012434,0.0001856515,0.9779919,0.0004442098,0.002356605],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02284808,"threshold_uncertainty_score":0.07643443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0857975271518483,"score_gpt":0.3199052363728755,"score_spread":0.2341077092210272,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}