{"id":"W4409423074","doi":"10.1016/j.buildenv.2025.112963","title":"Graph-based spatial–temporal prediction and feature interaction analysis of CO<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si54.svg\" display=\"inline\" id=\"d1e2369\"><mml:msub><mml:mrow/><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math> and occupant in large indoor space","year":2025,"lang":"lv","type":"article","venue":"Building and Environment","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Graph; Scalable Vector Graphics; Computer science; Theoretical computer science; World Wide Web","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":[0.0002901921,0.0006038391,0.0004721397,0.002036696,0.0004319429,0.0005703975,0.0007212758,0.0004561219,0.003738781],"category_scores_gemma":[0.0013811,0.0001977995,0.001008792,0.001693777,0.0002351912,0.0006018893,0.0004624206,0.0004822242,0.000924253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006939681,"about_ca_system_score_gemma":0.0008553516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07197151,"about_ca_topic_score_gemma":0.09529831,"domain_scores_codex":[0.9997726,0.00003383865,0.00001180501,0.00009150773,0.00004885688,0.00004141964],"domain_scores_gemma":[0.9993279,0.0003181289,0.00006952362,0.00007265432,0.0001740709,0.00003767762],"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":[0.0009888128,0.0004714378,0.0521793,0.0002498288,0.0003637871,0.000391898,0.0002407109,0.5632037,0.01454891,0.005769413,0.01682755,0.3447647],"study_design_scores_gemma":[0.000003828388,0.00001805555,0.003874446,0.00000284608,0.00001681658,0.00001574297,0.00002251101,0.9937688,0.0007894579,0.001011523,0.0004691371,0.000006901648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.37398,0.0007667983,0.6054048,0.0005834988,0.0001551575,0.000130835,0.006934916,0.006754644,0.005289282],"genre_scores_gemma":[0.9321827,0.0001479529,0.05881243,0.00004639942,0.00003939794,0.00006750727,0.005234466,0.0002021725,0.003266831],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07197151,"threshold_uncertainty_score":0.1431051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01035546067618219,"score_gpt":0.2315209400526183,"score_spread":0.2211654793764361,"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."}}