{"id":"W2901460997","doi":"10.1016/j.envint.2018.11.042","title":"A picture tells a thousand…exposures: Opportunities and challenges of deep learning image analyses in exposure science and environmental epidemiology","year":2018,"lang":"en","type":"article","venue":"Environment International","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":105,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of British Columbia; McGill University; McGill University Health Centre","funders":"Fonds de Recherche du Québec - Santé; British Columbia Lung Association; Natural Sciences and Engineering Research Council of Canada; Wellcome Trust; Canadian Institutes of Health Research; Cancer Research Society","keywords":"Environmental epidemiology; Epidemiology; Exposure assessment; Environmental health; Data science; Computer science; Medicine; Pathology","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":[0.009855011,0.0006700564,0.0007627709,0.001935722,0.0007191567,0.004399175,0.001412766,0.002588525,0.002433073],"category_scores_gemma":[0.01929669,0.0005704301,0.0008250095,0.001638811,0.00423986,0.008774086,0.003015167,0.00741599,0.0005809687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00217399,"about_ca_system_score_gemma":0.002115433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005809116,"about_ca_topic_score_gemma":0.007076235,"domain_scores_codex":[0.9979885,0.001057255,0.00008471472,0.0003112149,0.0004405704,0.0001178562],"domain_scores_gemma":[0.9818376,0.01356431,0.0007608035,0.001255203,0.001848308,0.0007337713],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002806659,0.0001813476,0.04586548,0.00125935,0.0006600912,0.0004296218,0.001443726,0.04091338,0.002841726,0.1760078,0.06198483,0.6681319],"study_design_scores_gemma":[0.0000220068,0.00008667279,0.0144699,0.001085815,0.0001106972,0.0003765487,0.001166689,0.1019236,0.001724062,0.8069046,0.07202496,0.0001043066],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06454635,0.0854146,0.4512508,0.3761974,0.002312413,0.0001077153,0.002403447,0.0008182328,0.01694896],"genre_scores_gemma":[0.5838845,0.08605748,0.2834468,0.0318983,0.004726398,0.0002652702,0.001768268,0.0003217038,0.007631305],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009855011,"threshold_uncertainty_score":0.05211896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0776304045609843,"score_gpt":0.3232593032122717,"score_spread":0.2456288986512874,"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."}}