{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00133448,0.0001343047,0.0004992441,0.0002206286,0.0001485017,0.00002134642,0.0001284298,0.0002953656,0.0001191598],"category_scores_gemma":[0.0002115396,0.00009966116,0.00006067846,0.0002129944,0.000187172,0.00006158805,0.0000190783,0.0006192585,0.000002307408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006169362,"about_ca_system_score_gemma":0.00006738675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009883067,"about_ca_topic_score_gemma":0.0001348375,"domain_scores_codex":[0.9986772,0.0002282147,0.0005049666,0.0001929508,0.00006968796,0.0003269985],"domain_scores_gemma":[0.9987136,0.0008310986,0.0002433797,0.0001207098,0.00002758756,0.00006361853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008858402,0.00003221822,0.9615518,0.0000445406,0.00006289469,0.00006141651,0.0007091541,0.001383458,0.0007675349,0.002112962,0.000006539386,0.0323816],"study_design_scores_gemma":[0.001089686,0.0002408538,0.9319641,0.00006738597,0.0001220004,0.0006316193,0.000695777,0.01557534,0.000448485,0.04865162,0.0003198055,0.0001933485],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915915,0.0005517422,0.0006207387,0.000281145,0.0004335861,0.00006775089,0.000001765214,0.00000938579,0.006442426],"genre_scores_gemma":[0.9916195,0.000111321,0.007857838,0.0003429904,0.00006239821,9.794927e-8,0.00000121097,0.000002600553,0.000002042545],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04653867,"threshold_uncertainty_score":0.4064064,"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."}}