{"id":"W4236330255","doi":"10.22360/springsim.2018.cns.012","title":"Predicting Indoor Temperature from Smart Thermostat and Weather Forecast Data","year":2017,"lang":"en","type":"article","venue":"","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Thermostat; HVAC; Artificial neural network; Computer science; Meteorology; Environmental science; Engineering; Machine learning; Air conditioning; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0001949016,0.0004581847,0.0003152631,0.0004048892,0.0001651601,0.0002721118,0.000230113,0.0002556237,0.0008753598],"category_scores_gemma":[0.0006733567,0.0001874142,0.000255471,0.0006772443,0.0001312812,0.000358007,0.0001745736,0.0002524162,0.0003970757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006934868,"about_ca_system_score_gemma":0.0007261003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.15421,"about_ca_topic_score_gemma":0.2546206,"domain_scores_codex":[0.9998832,0.00001162608,0.000005246767,0.00003495622,0.00004446552,0.00002052346],"domain_scores_gemma":[0.9997985,0.00004335748,0.00002138591,0.00002394094,0.00009834712,0.00001456888],"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":[0.0004070194,0.0001671571,0.1886086,0.0001252934,0.0001064969,0.0001705683,0.000133543,0.6922899,0.02574005,0.0003644645,0.001924744,0.08996211],"study_design_scores_gemma":[0.00001313244,0.00006448583,0.1276964,0.000007572625,0.0000274818,0.00002798831,0.00005629099,0.8621419,0.008912881,0.0001935058,0.000839259,0.00001901359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9791881,0.0000694088,0.01534248,0.00002831023,0.0000228579,0.00002727909,0.002349351,0.0005847453,0.002387393],"genre_scores_gemma":[0.991106,0.00005222766,0.006270422,0.000005528012,0.000004059543,0.00001244707,0.001843537,0.00001779188,0.0006879668],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.15421,"threshold_uncertainty_score":0.3066248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02699520025186577,"score_gpt":0.2402178436028153,"score_spread":0.2132226433509495,"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."}}