{"id":"W4394628736","doi":"10.1109/cloudnet59005.2023.10490084","title":"A Sensor Predictive Model for Power Consumption using Machine Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Internet of Things and Social Network Interactions","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ericsson (Canada); Concordia University","funders":"","keywords":"Computer science; Power consumption; Machine learning; Predictive power; Power demand; Consumption (sociology); Energy consumption; Power (physics); Artificial intelligence; Engineering; Electrical engineering","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":[0.000788705,0.00103856,0.001002008,0.0007135175,0.0003845854,0.0009276721,0.001658179,0.001023541,0.002014156],"category_scores_gemma":[0.002108755,0.0004850083,0.0007887212,0.0009774663,0.0004637645,0.001141931,0.000425955,0.001726881,0.000645056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001061882,"about_ca_system_score_gemma":0.000796793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01852905,"about_ca_topic_score_gemma":0.014727,"domain_scores_codex":[0.9995238,0.00008367061,0.00003129737,0.0001714063,0.0001257087,0.00006409041],"domain_scores_gemma":[0.9993215,0.0003948037,0.00006779865,0.00004603258,0.0001553832,0.00001455343],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002389528,0.00003483594,0.0006154522,0.00002324875,0.00001940288,0.00002697468,0.00001473635,0.9808856,0.0003671791,0.001225806,0.0004599968,0.01630286],"study_design_scores_gemma":[5.986766e-7,0.0000022868,0.00004805467,0.000001073045,0.00000149521,0.000001934748,7.828398e-7,0.9995198,0.00007845477,0.0002853785,0.00005901233,0.000001021282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03599221,0.0006169718,0.9570426,0.0004166963,0.0001174041,0.00006843673,0.000397611,0.001548159,0.003799928],"genre_scores_gemma":[0.9445148,0.000487005,0.04813109,0.0001470816,0.00008069282,0.0002237847,0.0005920282,0.00008019356,0.005743388],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01852905,"threshold_uncertainty_score":0.03684241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05317544984830897,"score_gpt":0.3158456666261463,"score_spread":0.2626702167778373,"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."}}