{"id":"W4386829292","doi":"10.1038/s41597-023-02535-y","title":"A daily high-resolution (1 km) human thermal index collection over the North China Plain from 2003 to 2020","year":2023,"lang":"en","type":"article","venue":"Scientific Data","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"National Natural Science Foundation of China","keywords":"China; Index (typography); Environmental science; Coastal plain; Geography; Remote sensing; Biology; Ecology; Archaeology; World Wide Web; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"not_applicable","genre":"dataset","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008008709,0.0001201379,0.00008889876,0.00006937425,0.001007803,0.000271414,0.001044513,0.00004729046,0.002382341],"category_scores_gemma":[0.0001281943,0.00009133754,0.00001823545,0.002576068,0.0002155716,0.0005182964,0.001070463,0.0001324778,0.003564421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001460349,"about_ca_system_score_gemma":0.00003167482,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00646271,"about_ca_topic_score_gemma":0.02158774,"domain_scores_codex":[0.997997,0.0001056221,0.0001932245,0.0007661252,0.0006082077,0.000329846],"domain_scores_gemma":[0.9983889,0.00003095751,0.00006071855,0.001406717,0.00001169837,0.0001009816],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00001145268,0.00002790137,0.06735595,0.000001186106,0.000007118125,0.000003219847,0.0007730233,0.001167074,0.01180056,0.00001410463,0.9174631,0.001375299],"study_design_scores_gemma":[0.0002067191,0.00002019789,0.9169797,0.00000640354,0.00001175827,5.404648e-7,0.00005167868,0.01482618,0.0002483205,0.0001853334,0.06734252,0.0001207035],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928696,0.000008468101,0.0002865762,0.0005537228,0.001376077,0.0004735501,0.003304008,0.0001007406,0.001027275],"genre_scores_gemma":[0.9809821,0.000001729802,0.0001436918,0.0001283353,0.000145006,0.00003082307,0.008981004,0.00001452065,0.009572838],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8501206,"threshold_uncertainty_score":0.9985296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02063176456647712,"score_gpt":0.2386361082318871,"score_spread":0.21800434366541,"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."}}