{"id":"W2150257654","doi":"10.1021/es103054q","title":"Detection of the Spatiotemporal Trends of Mercury in Lake Erie Fish Communities: A Bayesian Approach","year":2011,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of the Environment, Conservation and Parks; Environment and Climate Change Canada; University of Toronto","funders":"","keywords":"Perch; Stizostedion; Fishery; Micropterus; Morone; Bass (fish); Forage fish; Fishing; Environmental science; Fillet (mechanics); Biology; Fish <Actinopterygii>; Engineering","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":[0.002425259,0.000339645,0.0003910478,0.002619013,0.0004407703,0.0009035351,0.0007084284,0.0006699574,0.0005317065],"category_scores_gemma":[0.006657503,0.0003932384,0.0005757098,0.001214157,0.0003341808,0.0007557101,0.000949973,0.0004245008,0.00013399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009203473,"about_ca_system_score_gemma":0.001019458,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04019093,"about_ca_topic_score_gemma":0.05443997,"domain_scores_codex":[0.999261,0.0002729438,0.00006694965,0.0002367937,0.0001026031,0.00005975406],"domain_scores_gemma":[0.9981762,0.000912946,0.000389179,0.00008627864,0.0003604951,0.00007482855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000303767,0.0001730863,0.6965419,0.0002127312,0.0006863092,0.0002723586,0.001142384,0.1518503,0.01408074,0.005614389,0.0007637265,0.1283583],"study_design_scores_gemma":[0.00002661377,0.0001135639,0.307335,0.00006100248,0.0001584662,0.0001903286,0.000514842,0.6829304,0.00117935,0.005848929,0.001554241,0.00008722458],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8372692,0.0003996851,0.1592351,0.0002479701,0.000004270799,0.0000731598,0.0009864321,0.0001648331,0.00161925],"genre_scores_gemma":[0.9477548,0.0001765892,0.05045709,0.00003700547,0.00001025958,0.00007866639,0.0009723447,0.00001444593,0.0004986611],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9598091,"threshold_uncertainty_score":0.07991397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0173975720274551,"score_gpt":0.2198868162537342,"score_spread":0.2024892442262791,"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."}}