{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000187887,0.00007527619,0.00008771522,0.00008131668,0.0002513362,0.0001122034,0.0001810455,0.00004669787,0.00002131037],"category_scores_gemma":[0.00004993853,0.00007070204,0.00008482266,0.0001291769,0.00001618448,0.0003206553,0.0001135677,0.0001259828,0.00005422151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005189087,"about_ca_system_score_gemma":0.0000207722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005070656,"about_ca_topic_score_gemma":0.00001510966,"domain_scores_codex":[0.9993365,0.00002408375,0.0001243089,0.000197512,0.0001118848,0.0002057352],"domain_scores_gemma":[0.9996011,0.0001071904,0.00005680408,0.0001028291,0.00009167191,0.00004037504],"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":[0.00006133724,0.0000633004,0.001524255,0.00002291887,0.0001295743,0.000008565986,0.01116482,0.8388205,0.003136538,0.1365746,0.006993676,0.001499941],"study_design_scores_gemma":[0.0001286938,0.00004208388,0.00005530804,0.00001313777,0.000005172507,0.000004207342,0.00006217047,0.9964101,0.00009966081,0.002065292,0.0010315,0.00008268338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04714378,0.000007628579,0.9502621,0.000376673,0.0004572488,0.0001422333,0.000004920772,0.0004323552,0.001173031],"genre_scores_gemma":[0.9179333,0.000009555752,0.06938893,0.0002566437,0.00007345862,0.00001661861,0.000007064581,0.0000120433,0.0123024],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8808732,"threshold_uncertainty_score":0.2883146,"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."}}