{"id":"W2527076671","doi":"10.1007/s00484-016-1248-2","title":"When evidence of heat-related vulnerability depends on the contrast measure","year":2016,"lang":"en","type":"article","venue":"International Journal of Biometeorology","topic":"Climate Change and Health Impacts","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Institut National de Santé Publique du Québec","keywords":"Contrast (vision); Percentile; Vulnerability (computing); Extreme heat; Measure (data warehouse); Statistics; Demography; Population; Econometrics; Medicine; Mathematics; Climate change; Environmental health; Computer science; Biology; Data mining; Ecology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001662676,0.0000849112,0.0001820887,0.0001154443,0.00003680773,0.000008815838,0.0005443154,0.00007884792,0.005670314],"category_scores_gemma":[0.0009500481,0.00004130033,0.00009173859,0.0001004849,0.0002991836,0.0001901674,0.00008601432,0.0001442002,0.0001066946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002048631,"about_ca_system_score_gemma":0.00002805549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007777096,"about_ca_topic_score_gemma":0.0000615743,"domain_scores_codex":[0.998437,0.0002149496,0.0005011473,0.0001173893,0.0005508912,0.0001786317],"domain_scores_gemma":[0.9981974,0.001098557,0.0003347868,0.0001445711,0.0001259521,0.00009873213],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.002553952,0.000427892,0.185354,0.00001049838,0.0002498182,0.00008433447,0.001346515,0.00008522084,0.6630004,0.001091556,0.01205759,0.1337382],"study_design_scores_gemma":[0.004955415,0.005719356,0.8661422,0.001209627,0.0001044334,0.001218893,0.0002255037,0.0001966499,0.07397079,0.03705239,0.008804911,0.0003998464],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9329281,0.0001628608,0.0002804905,0.06513182,0.0006718261,0.0000738601,0.00001582314,0.00000395706,0.000731244],"genre_scores_gemma":[0.9984592,0.0001740359,0.00007133235,0.001145768,0.00009757085,0.00000187454,3.639354e-7,0.000005229486,0.00004469338],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6807882,"threshold_uncertainty_score":0.9952387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06946494269273829,"score_gpt":0.3372618247285716,"score_spread":0.2677968820358333,"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."}}