{"id":"W6950407456","doi":"10.5281/zenodo.8192913","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":"Parallel Computing and Optimization Techniques","field":"Computer Science","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":[0.0004939756,0.002047769,0.001318737,0.001700208,0.0005556195,0.0009131355,0.001501061,0.001063095,0.02063912],"category_scores_gemma":[0.002275207,0.0004818141,0.0009095454,0.003157798,0.0002793992,0.0009283114,0.0007003036,0.001106407,0.02750833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007362394,"about_ca_system_score_gemma":0.0007140137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01014985,"about_ca_topic_score_gemma":0.01789486,"domain_scores_codex":[0.9990445,0.0001143775,0.00007396648,0.0003772497,0.0002600771,0.0001298517],"domain_scores_gemma":[0.9987213,0.0001851545,0.00007337735,0.0004120796,0.0005135039,0.00009464849],"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":[0.0005861282,0.0002161761,0.005566654,0.0009026987,0.0001364697,0.000108547,0.00005449242,0.007005144,0.001930359,0.0005647808,0.9482799,0.03464881],"study_design_scores_gemma":[0.0004666218,0.0003578108,0.07882839,0.0003444064,0.0001644896,0.0004456149,0.0002216669,0.03490804,0.01148476,0.004465928,0.8681331,0.0001792291],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01644414,0.0008079791,0.003924931,0.0003303972,0.0002986616,0.0001174151,0.9599949,0.01132471,0.006756851],"genre_scores_gemma":[0.01547134,0.0001633941,0.002353652,0.000118998,0.0000480549,0.0001598834,0.9791467,0.0004967581,0.002041262],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02063912,"threshold_uncertainty_score":0.06904477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04718754379963328,"score_gpt":0.2794759539084999,"score_spread":0.2322884101088667,"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."}}