{"id":"W2098805488","doi":"10.1016/j.jglr.2013.12.017","title":"Significance of toxaphene in Great Lakes fish consumption advisories","year":2014,"lang":"en","type":"article","venue":"Journal of Great Lakes Research","topic":"Toxic Organic Pollutants Impact","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of the Environment, Conservation and Parks; University of Windsor; University of Toronto","funders":"Health Canada; Government of Ontario; Ministry of Natural Resources","keywords":"Toxaphene; Environmental science; Mercury (programming language); Fish <Actinopterygii>; Fishery; Fish consumption; Toxicology; Pesticide; Ecology; Biology; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.003089617,0.0002661207,0.0001979504,0.0009362489,0.000547268,0.001651857,0.0003263876,0.0004256449,0.002935465],"category_scores_gemma":[0.01009703,0.0001490737,0.0002126488,0.001307173,0.0002459968,0.0008616019,0.0005692293,0.0005678369,0.0002853435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001673766,"about_ca_system_score_gemma":0.004925298,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08991031,"about_ca_topic_score_gemma":0.2648594,"domain_scores_codex":[0.9988286,0.0005450135,0.0001306323,0.00006718811,0.000363165,0.00006538879],"domain_scores_gemma":[0.9936427,0.00308276,0.0008424032,0.0001452944,0.002025801,0.000261099],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001132971,0.0002593236,0.6742332,0.0007876594,0.0003474196,0.0003731076,0.0008030218,0.02322895,0.004675704,0.004387092,0.01199802,0.2777736],"study_design_scores_gemma":[0.0001762186,0.0009593257,0.7781155,0.001743533,0.0008521425,0.000306944,0.007169904,0.07838487,0.01813033,0.01055425,0.1034615,0.0001454328],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8803387,0.005622878,0.008918811,0.01778989,0.0003793541,0.0002788725,0.006681996,0.0002750716,0.07971434],"genre_scores_gemma":[0.9845245,0.002192572,0.007359917,0.0005938766,0.00006620647,0.00002724753,0.0007160569,0.00002004292,0.004499531],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9100897,"threshold_uncertainty_score":0.178774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04512993111098001,"score_gpt":0.3267947268401733,"score_spread":0.2816647957291933,"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."}}