{"id":"W4402548399","doi":"10.1016/j.buildenv.2024.112088","title":"Combining visual intelligence and social-physical urban features facilitates fine-scale seasonality characterization of urban thermal environments","year":2024,"lang":"en","type":"article","venue":"Building and Environment","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"Natural Science Foundation of Henan Province; Science and Technology Department of Gansu Province; China Postdoctoral Science Foundation","keywords":"Seasonality; Scale (ratio); Characterization (materials science); Environmental science; Computer science; Geography; Meteorology; Remote sensing; Cartography; Machine learning; Materials science; Nanotechnology","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":[0.0001923043,0.0004176897,0.0003850505,0.001870246,0.0002267098,0.001388037,0.0002884329,0.0002757604,0.002334894],"category_scores_gemma":[0.0006470865,0.0001966795,0.0004156124,0.001106067,0.0002359056,0.0008507602,0.0010661,0.0003334227,0.000763806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001863528,"about_ca_system_score_gemma":0.0001888486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003876054,"about_ca_topic_score_gemma":0.0101096,"domain_scores_codex":[0.9998598,0.00001566529,0.000004808039,0.00004970776,0.0000393929,0.00003066887],"domain_scores_gemma":[0.9997416,0.00006170552,0.00004422711,0.00004565536,0.00007225287,0.00003454866],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004704169,0.0003049957,0.08285468,0.0004755873,0.0002680741,0.0002653963,0.0008771773,0.08030498,0.2328742,0.002979291,0.007169518,0.5911557],"study_design_scores_gemma":[0.00002145364,0.0001893314,0.3015846,0.00006324059,0.0001675329,0.0003657598,0.001408925,0.6447124,0.03348908,0.00805827,0.009828675,0.0001106086],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.552761,0.0005021556,0.4152981,0.0002444246,0.0001227475,0.00014074,0.003000026,0.004227208,0.02370361],"genre_scores_gemma":[0.9484346,0.0001827583,0.0488138,0.00006030001,0.00004175127,0.00002718357,0.001064565,0.0001852607,0.001189797],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003876054,"threshold_uncertainty_score":0.00781101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01059507845261757,"score_gpt":0.2306547050542906,"score_spread":0.220059626601673,"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."}}