{"id":"W2315171566","doi":"10.1177/1420326x14539693","title":"Prediction of the thermal comfort indices using improved support vector machine classifiers and nonlinear kernel functions","year":2014,"lang":"en","type":"article","venue":"Indoor and Built Environment","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal General Hospital","funders":"","keywords":"Support vector machine; Thermal comfort; Artificial intelligence; Polynomial kernel; Nonlinear system; Machine learning; Computer science; Kernel (algebra); Thermal; Engineering; Kernel method; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007653838,0.00009242765,0.00009004692,0.00002705947,0.00009097294,0.00001115059,0.00004955153,0.00006707559,0.00002916229],"category_scores_gemma":[0.000002454047,0.00006925774,0.00002472261,0.00003378238,0.0000727205,0.0000679202,0.00004080428,0.00009522263,2.471462e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002249819,"about_ca_system_score_gemma":0.00000443933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003670919,"about_ca_topic_score_gemma":0.000004639424,"domain_scores_codex":[0.9995657,0.00001390294,0.0001362826,0.0001034044,0.00008367934,0.00009705146],"domain_scores_gemma":[0.9997818,0.0000127726,0.00004115213,0.0001269598,0.000002378683,0.00003497572],"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":[0.0000286838,0.00006561782,0.1511083,0.00006503538,0.0001086339,3.243087e-7,0.0002458134,0.7667252,0.0688084,0.00008652822,0.00004570895,0.0127117],"study_design_scores_gemma":[0.0003794495,0.00005998347,0.1022857,0.00001188475,0.00005099507,0.000006524209,0.00002276362,0.8899199,0.004728752,0.00001174456,0.002436555,0.00008582052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9801645,0.00009170482,0.01916869,0.00002680276,0.0001774662,0.00009109293,0.00002818836,0.0000323796,0.0002191175],"genre_scores_gemma":[0.9989401,0.00007354197,0.0008005833,0.00002503018,0.00005492419,0.00000716512,0.00001716395,0.00001428752,0.00006724733],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1231946,"threshold_uncertainty_score":0.2824249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01069216679270436,"score_gpt":0.1745759329960176,"score_spread":0.1638837662033132,"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."}}