{"id":"W3136866223","doi":"10.1061/(asce)me.1943-5479.0000923","title":"Multidomain Drivers of Occupant Comfort, Productivity, and Well-Being in Buildings: Insights from an Exploratory and Explanatory Analysis","year":2021,"lang":"en","type":"article","venue":"Journal of Management in Engineering","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Productivity; Computer science; Context (archaeology); Random forest; Quality (philosophy); Thermal comfort; Machine learning; Artificial intelligence; Geography","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":[0.0002056203,0.0001181576,0.0002832702,0.0007469534,0.00001336104,0.00002086877,0.00007870461,0.00004640179,0.000003027081],"category_scores_gemma":[0.000009853416,0.0001293281,0.00003592066,0.0005116963,0.00001212427,0.0004266061,0.00005420329,0.000159781,2.476367e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000585745,"about_ca_system_score_gemma":0.000005386664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009008881,"about_ca_topic_score_gemma":0.00008627163,"domain_scores_codex":[0.9992325,0.0000251275,0.0003502618,0.0001346302,0.0001443167,0.0001132074],"domain_scores_gemma":[0.9996997,0.00003604752,0.00007374246,0.0001201962,0.00002124317,0.00004906466],"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":[0.000008582486,0.00003580709,0.01982989,0.00009987749,0.0002970121,0.0001289841,0.0009814312,0.9754173,0.001771446,0.0005206006,0.000003905623,0.0009051363],"study_design_scores_gemma":[0.001377399,0.00004061643,0.0667706,0.0004428489,0.0003217379,0.00001162923,0.001779384,0.9202629,0.007865283,0.0004771057,0.0002863523,0.0003640849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9724875,0.0007873602,0.02644586,0.000008365636,0.0001242492,0.00004313641,6.26875e-7,0.00001900013,0.00008386754],"genre_scores_gemma":[0.9887632,0.0007985753,0.01038247,0.000006254297,0.00002523398,0.000002727985,0.000003946292,0.00001502964,0.000002527662],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05515437,"threshold_uncertainty_score":0.5273845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004454773682254938,"score_gpt":0.1912924350836937,"score_spread":0.1868376614014388,"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."}}