{"id":"W2567827994","doi":"10.1002/met.1606","title":"Observed changes in temperature extremes for the Beijing–Tianjin–Hebei region of China","year":2017,"lang":"en","type":"article","venue":"Meteorological Applications","topic":"Climate variability and models","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada; National Key Research and Development Program of China; Higher Education Discipline Innovation Project","keywords":"Beijing; Environmental science; Climate change; Climatology; Context (archaeology); Global warming; China; Frost (temperature); Geography; Physical geography; Meteorology; Oceanography; 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":[0.0002351441,0.0002172923,0.0001862128,0.0006887573,0.0003270404,0.0003324068,0.0002552297,0.0002190294,0.0006372259],"category_scores_gemma":[0.000287486,0.00009717209,0.000204084,0.000987623,0.0002076026,0.0002199922,0.0002313842,0.000134828,0.0001018817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007371613,"about_ca_system_score_gemma":0.0004947041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05268105,"about_ca_topic_score_gemma":0.0671768,"domain_scores_codex":[0.999879,0.00001896585,0.000013807,0.00003702605,0.0000274326,0.0000237734],"domain_scores_gemma":[0.9996997,0.00004104519,0.00007531159,0.00002765535,0.00008638998,0.00006989631],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001032245,0.00002292312,0.9872836,0.00002579821,0.0001121943,0.000156503,0.0003878431,0.001275735,0.005117269,0.00006145483,0.0003180204,0.005135454],"study_design_scores_gemma":[0.00000142514,0.00001001546,0.9991997,7.046027e-7,0.000006591849,0.00001288828,0.00008160636,0.0004146991,0.0001679986,0.000004559362,0.0000980219,0.000001815921],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991731,0.0000306514,0.00007625731,0.00001038506,0.000001859205,0.000003582403,0.0003627449,0.000004988142,0.0003364194],"genre_scores_gemma":[0.9993277,0.00001569347,0.00006545967,0.00000355982,0.000001756748,0.000006864967,0.0004435208,6.747197e-7,0.0001347505],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05268105,"threshold_uncertainty_score":0.1047488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08258081660380946,"score_gpt":0.2842759754135588,"score_spread":0.2016951588097494,"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."}}