{"id":"W4410435307","doi":"10.1175/jamc-d-24-0208.1","title":"Detecting Peri-Urban Climates in China Using a Thermal Variability Framework","year":2025,"lang":"en","type":"article","venue":"Journal of Applied Meteorology and Climatology","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Environmental science; China; Climatology; Meteorology; Geography; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000467229,0.0002594801,0.0001884636,0.001953447,0.0004402737,0.0008307343,0.0003009691,0.0001501397,0.000583129],"category_scores_gemma":[0.0004765377,0.0001194572,0.0002855917,0.002135882,0.0003351341,0.0003784439,0.0006047636,0.0001685053,0.0000723873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008221893,"about_ca_system_score_gemma":0.0005186339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0320931,"about_ca_topic_score_gemma":0.04417363,"domain_scores_codex":[0.9997852,0.00005105424,0.00001453824,0.00005943358,0.00004282065,0.0000470083],"domain_scores_gemma":[0.9996704,0.0000601409,0.00008323565,0.00004151846,0.00008862603,0.0000561115],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006370933,0.00005141548,0.9539965,0.00004114933,0.00008363515,0.0002173605,0.0005682809,0.01459647,0.003255107,0.001736267,0.0006547195,0.02473532],"study_design_scores_gemma":[0.000002632753,0.00002144525,0.9692059,0.000005509404,0.00001431342,0.00002872518,0.0004446949,0.02868524,0.0003146942,0.0004522334,0.0008135432,0.00001109295],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995244,0.0000651353,0.002485498,0.00004715446,0.000003624892,0.00001650528,0.0005733296,0.00003199936,0.001532727],"genre_scores_gemma":[0.9990326,0.0000176003,0.0005457503,0.00000469586,0.000002978361,0.000007964719,0.0002692109,0.000002448779,0.0001167709],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0320931,"threshold_uncertainty_score":0.06381261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007986805409549715,"score_gpt":0.2381663818553146,"score_spread":0.2301795764457649,"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."}}