{"id":"W4231520321","doi":"10.1503/cmaj.170204","title":"The interwoven history of mercury poisoning in Ontario and Japan","year":2017,"lang":"en","type":"erratum","venue":"Canadian Medical Association Journal","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mercury (programming language); Mercury poisoning; Data science; Computer science; World Wide Web; Medicine; Internal medicine; Toxicity","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00170542,0.0002618536,0.0004413602,0.002373304,0.006491295,0.001685056,0.0008354919,0.001958758,0.006209882],"category_scores_gemma":[0.00587432,0.0003125529,0.0003067337,0.005450909,0.003255258,0.001536834,0.001697869,0.003446166,0.000546334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03027747,"about_ca_system_score_gemma":0.03192896,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8929002,"about_ca_topic_score_gemma":0.9633601,"domain_scores_codex":[0.997798,0.0002175047,0.0002298535,0.0001915657,0.001211387,0.0003518091],"domain_scores_gemma":[0.995278,0.0009567126,0.0008240633,0.0001572827,0.002196662,0.0005873004],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002426813,0.00005297248,0.07334599,0.001120932,0.0000822781,0.01048143,0.02456328,0.00009462749,0.0007674054,0.01663208,0.7855467,0.08706964],"study_design_scores_gemma":[0.00001411328,0.00003393882,0.1148494,0.001301377,0.00009273533,0.003963369,0.007781826,0.00004302882,0.0002431176,0.001610389,0.8700089,0.00005785027],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.08273769,0.2044187,0.0006193426,0.5440679,0.043405,0.00008468796,0.006136523,0.0000727433,0.1184574],"genre_scores_gemma":[0.5502997,0.2460633,0.001659342,0.08624012,0.0278952,0.00009278202,0.003712224,0.0001704246,0.08386692],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1070998,"threshold_uncertainty_score":0.2196794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0120901911452556,"score_gpt":0.2247417409372527,"score_spread":0.2126515497919971,"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."}}