{"id":"W2884963234","doi":"10.1080/14634988.2018.1498272","title":"Assessing fish consumption Beneficial Use Impairment at Great Lakes Areas of Concern: Toronto case study","year":2018,"lang":"en","type":"article","venue":"Aquatic Ecosystem Health & Management","topic":"Toxic Organic Pollutants Impact","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; Fisheries and Oceans Canada; University of Windsor; Ministry of the Environment, Conservation and Parks","funders":"","keywords":"Consumption (sociology); Fish consumption; Remedial action; Fishery; Environmental science; Fish <Actinopterygii>; Geography; Environmental protection; Environmental health; Ecology; Medicine; Biology; Contamination; Environmental remediation","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001117979,0.0004283405,0.0001668813,0.001142028,0.002333454,0.0009460963,0.0008326771,0.0006474843,0.001774735],"category_scores_gemma":[0.002292655,0.0002631234,0.0004606612,0.001957399,0.001164234,0.0004983596,0.001280556,0.0004959319,0.0001447642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02638119,"about_ca_system_score_gemma":0.01332542,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9312031,"about_ca_topic_score_gemma":0.9817623,"domain_scores_codex":[0.9988741,0.0002229415,0.00006874916,0.00009826088,0.0004974158,0.0002386029],"domain_scores_gemma":[0.997493,0.000342793,0.0003797023,0.00013775,0.001337538,0.0003093189],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002657799,0.0002356063,0.8826993,0.000492161,0.0001969781,0.01605351,0.0298683,0.009225885,0.007725568,0.002555239,0.01065931,0.04002244],"study_design_scores_gemma":[0.00002249943,0.0003555317,0.9274812,0.0001748253,0.0001478781,0.001687986,0.04580671,0.006219211,0.002178207,0.0004095615,0.01544282,0.0000734164],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9860132,0.0002320376,0.0008547799,0.0007674509,0.00001007757,0.0002667682,0.001134242,0.00002594017,0.01069554],"genre_scores_gemma":[0.9914253,0.0004139776,0.002581553,0.0002054186,0.000006251841,0.0001006032,0.0006768305,0.000008007412,0.00458205],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06879693,"threshold_uncertainty_score":0.1914098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05213745284036855,"score_gpt":0.3303691471657734,"score_spread":0.2782316943254048,"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."}}