{"id":"W1989705698","doi":"10.4028/www.scientific.net/amm.235.216","title":"A Remote Intelligent Control Method Based on the Thermal Comfort Model","year":2012,"lang":"en","type":"article","venue":"Applied Mechanics and Materials","topic":"Refrigeration and Air Conditioning Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Thermal comfort; Air conditioning; Airflow; Engineering; Intelligent control; Thermal; Control engineering; Simulation; Computer science; Control (management); Automotive engineering; Artificial intelligence; Mechanical engineering; Meteorology","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":[0.0003000028,0.0004469685,0.0004521865,0.0002634025,0.0002847997,0.0004670045,0.0006747893,0.0003713869,0.002459303],"category_scores_gemma":[0.0005352683,0.0001502791,0.0004836999,0.0001397109,0.0002921458,0.0007073602,0.0003466179,0.0004609404,0.0003448501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000253125,"about_ca_system_score_gemma":0.0002655656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001436944,"about_ca_topic_score_gemma":0.001037181,"domain_scores_codex":[0.9996081,0.00006114596,0.00001521059,0.0001238768,0.0001616577,0.00002993717],"domain_scores_gemma":[0.9998534,0.00003849267,0.00002197718,0.00002820627,0.00004808941,0.000009931689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000347632,0.0002423909,0.001279784,0.0002950689,0.00009728429,0.0001778496,0.0004253961,0.2160948,0.1806391,0.01908714,0.002372436,0.5789411],"study_design_scores_gemma":[0.00003466506,0.0002508053,0.0006974716,0.00001097408,0.00003815881,0.0001393006,0.00002312459,0.9733739,0.02023307,0.001657065,0.003510991,0.00003047176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01471789,0.0001570725,0.9802929,0.00004372524,0.00002981427,0.00003230554,0.000007700876,0.0007250124,0.003993545],"genre_scores_gemma":[0.8774833,0.0001609269,0.1170521,0.00007303634,0.00005215681,0.0001045521,0.00003721299,0.00008549096,0.004951205],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002459303,"threshold_uncertainty_score":0.008227229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01745577721582111,"score_gpt":0.2296601306909192,"score_spread":0.2122043534750981,"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."}}