{"id":"W2573541171","doi":"10.1101/099770","title":"Genomic and Environmental Contributions to Chronic Diseases in Urban Populations","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada; Université de Montréal; University of Toronto; Statistics Canada; Ontario Institute for Cancer Research","funders":"Medical Research Council; Fonds de Recherche du Québec - Santé; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Genomics; Computational biology; Genome; Biology; Evolutionary biology; Principal (computer security); Genetics; Data science; Computer science; Gene","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":[0.0006214295,0.0003082176,0.0002166385,0.0006268722,0.000438464,0.0006239684,0.0002328755,0.000373098,0.002807997],"category_scores_gemma":[0.001220649,0.0002007395,0.0004728348,0.0009190982,0.0006853826,0.0002703551,0.001005443,0.0004480159,0.0001172973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003334272,"about_ca_system_score_gemma":0.0004609248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006021595,"about_ca_topic_score_gemma":0.008589265,"domain_scores_codex":[0.9995894,0.0001637416,0.0000165449,0.0001349384,0.00003950947,0.00005579657],"domain_scores_gemma":[0.9994928,0.0001702709,0.000148507,0.00008731175,0.00004098742,0.00006021297],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000232147,0.000128747,0.9733297,0.00007560703,0.0006272328,0.0001679229,0.0005443014,0.004045424,0.005937431,0.004187959,0.0003985976,0.01032492],"study_design_scores_gemma":[0.000009478403,0.00005893303,0.9932833,0.00001048429,0.0001484825,0.0000791585,0.0003132643,0.001672965,0.0006397592,0.003194049,0.0005821314,0.000008029507],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99582,0.0003860601,0.001638499,0.0003826699,0.000008442773,0.000006692414,0.0006302256,0.0000151639,0.001112217],"genre_scores_gemma":[0.9986975,0.0002214308,0.0006062905,0.0000434329,0.00001005904,0.00001159417,0.0002013163,0.000005281465,0.0002032243],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006021595,"threshold_uncertainty_score":0.01197308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01360374772086804,"score_gpt":0.2475488607215164,"score_spread":0.2339451130006483,"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."}}