{"id":"W2168223088","doi":"10.2166/wqrj.2006.001","title":"Modelling Human Exposure of Methylmercury from Fish Consumption","year":2006,"lang":"en","type":"article","venue":"Water Quality Research Journal","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Ministry of Environment; Minnesota Department of Health","keywords":"Methylmercury; Bioaccumulation; Rainbow trout; Environmental science; Fish <Actinopterygii>; Perch; Fishery; Mercury (programming language); Contamination; Toxicology; Ecology; Environmental chemistry; Biology; Chemistry; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004025153,0.00009335785,0.0001916434,0.00007427351,0.0005130884,0.0000801235,0.0002131996,0.00006059762,0.004254519],"category_scores_gemma":[0.00003799352,0.00006451985,0.0001020966,0.00009168401,0.0004104389,0.0003071313,0.0001573713,0.0004362142,0.000224317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001110939,"about_ca_system_score_gemma":0.000009679912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0043967,"about_ca_topic_score_gemma":0.0002279699,"domain_scores_codex":[0.9970303,0.0008197934,0.0005122893,0.0001715861,0.001056104,0.0004099398],"domain_scores_gemma":[0.999386,0.0001390153,0.00009396605,0.0001808844,0.00007843824,0.0001216588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000664184,0.0001901149,0.2968749,0.00002630893,0.00006684836,0.00001305315,0.004038523,0.007543449,0.67678,0.0002240105,0.01273801,0.001438423],"study_design_scores_gemma":[0.002238292,0.0003880679,0.356187,0.0001308948,0.00005035722,0.00003331228,0.002369698,0.001183182,0.5086935,0.1150974,0.01302105,0.0006071714],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949281,0.00009278268,0.00189046,0.0004720356,0.0000585421,0.00008922414,0.00002377786,0.00001030364,0.002434784],"genre_scores_gemma":[0.9981195,0.0000764185,0.0006972225,0.00003447135,0.0001570947,0.000004110841,0.00002262954,0.000008204728,0.0008803271],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1680865,"threshold_uncertainty_score":0.9966557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.225677970950089,"score_gpt":0.4208041458800979,"score_spread":0.1951261749300089,"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."}}