{"id":"W4412515242","doi":"10.3390/buildings15142539","title":"Personalized Human Thermal Sensation Prediction Based on Bayesian-Optimized Random Forest","year":2025,"lang":"en","type":"article","venue":"Buildings","topic":"Color perception and design","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China; Natural Science Foundation of Xiamen City","keywords":"Random forest; Thermal sensation; Bayesian probability; Artificial intelligence; Computer science; Thermal; Machine learning; Thermal comfort; Meteorology; Physics","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":[0.0009745581,0.0006787951,0.0008625585,0.0005192939,0.0002635507,0.0004253474,0.0007311626,0.000567591,0.001025149],"category_scores_gemma":[0.001896099,0.0003780339,0.0006998976,0.0004240317,0.0002797775,0.0007716027,0.0003391971,0.000635171,0.0003175263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004321624,"about_ca_system_score_gemma":0.0007367381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01004438,"about_ca_topic_score_gemma":0.01305853,"domain_scores_codex":[0.9996506,0.0001064479,0.0000119629,0.00009993072,0.00007479371,0.00005623908],"domain_scores_gemma":[0.999312,0.0003826431,0.00005989456,0.00005103674,0.0001662696,0.00002805866],"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":[0.00006906398,0.0000506647,0.001237648,0.00002590273,0.00002336105,0.00002337509,0.00002276327,0.9554858,0.001902614,0.001036745,0.0004537931,0.0396683],"study_design_scores_gemma":[0.00000200199,0.000007654047,0.0001914501,0.000001331442,0.000003234592,0.000003619404,0.000001330641,0.9991854,0.0001729131,0.0003913246,0.00003699891,0.000002695866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07518215,0.0002854761,0.9223334,0.00009537244,0.00002585355,0.00003178761,0.00008761605,0.0006447179,0.001313643],"genre_scores_gemma":[0.9212652,0.0001540279,0.07718518,0.00007069999,0.00002248002,0.00006477336,0.0001920876,0.00005863221,0.0009868887],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01004438,"threshold_uncertainty_score":0.01997179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0195409847390242,"score_gpt":0.3109981565520436,"score_spread":0.2914571718130194,"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."}}