{"id":"W2794910998","doi":"10.1016/j.ssmph.2018.03.007","title":"Machine learning in social epidemiology: Learning from experience","year":2018,"lang":"en","type":"article","venue":"SSM - Population Health","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"","keywords":"Social learning; Epidemiology; Psychology; Computer science; Artificial intelligence; Knowledge management; Medicine","routes":{"ca_aff":true,"ca_fund":false,"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":[0.007124332,0.0005989759,0.001073707,0.001840642,0.0004602234,0.002536411,0.001215893,0.001444472,0.002233419],"category_scores_gemma":[0.04426971,0.0004648824,0.0008539081,0.001390727,0.002009091,0.004958468,0.002022511,0.002555046,0.0003298755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007876025,"about_ca_system_score_gemma":0.0006238608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002328967,"about_ca_topic_score_gemma":0.002574729,"domain_scores_codex":[0.9977896,0.001515642,0.0001205576,0.0002951009,0.0001982102,0.00008085417],"domain_scores_gemma":[0.9518687,0.04365338,0.001189597,0.001906259,0.0008675763,0.0005145085],"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":[0.0002691463,0.0006524523,0.08484062,0.0009595126,0.0007529121,0.0003804832,0.001773964,0.1940846,0.001025062,0.1788448,0.007041864,0.5293745],"study_design_scores_gemma":[0.00004045084,0.0001999076,0.008523176,0.0001832785,0.00006985166,0.0001533859,0.0003096919,0.5896237,0.0004209771,0.3967798,0.00364823,0.00004757531],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1223045,0.006418502,0.856316,0.008207938,0.0003109893,0.0001134141,0.0004235113,0.0002817749,0.005623338],"genre_scores_gemma":[0.9037874,0.002763442,0.08984192,0.0005525789,0.0004969306,0.0001515752,0.0003212487,0.00003691994,0.002047969],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007124332,"threshold_uncertainty_score":0.03767753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07282738971133168,"score_gpt":0.3731319701535039,"score_spread":0.3003045804421722,"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."}}