{"id":"W2020790654","doi":"10.1016/j.envres.2003.09.008","title":"Quantifying the fate of mercury in the Great Lakes Basin: toward an ecosystem approach","year":2003,"lang":"en","type":"article","venue":"Environmental Research","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Trent University","funders":"","keywords":"Mercury (programming language); Ecosystem; Environmental science; Structural basin; Human health; Ecology; Environmental health; Biology; Computer science","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":[0.0007120751,0.0004311599,0.0003515282,0.0008799717,0.0006222032,0.001337819,0.000402949,0.0007232492,0.0002894232],"category_scores_gemma":[0.0006895626,0.0002846902,0.0003087725,0.001019043,0.0004618814,0.001610265,0.0007812827,0.000265138,0.00004955909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001257942,"about_ca_system_score_gemma":0.002369292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07099131,"about_ca_topic_score_gemma":0.131476,"domain_scores_codex":[0.9997782,0.00009607979,0.0000130415,0.00004140403,0.00004955494,0.00002169846],"domain_scores_gemma":[0.999856,0.00003833897,0.00003305373,0.00001611691,0.00004247268,0.00001406509],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001590432,0.0002191539,0.786332,0.0003123608,0.0007991716,0.0002303827,0.000867634,0.06581056,0.06707792,0.003299884,0.0006379808,0.07425388],"study_design_scores_gemma":[0.00003499918,0.0003833387,0.8353651,0.00005958446,0.0006208292,0.0002133753,0.002363757,0.1133207,0.03053888,0.0107675,0.006260927,0.00007122898],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9936858,0.0005981785,0.003042488,0.0004719187,0.000003987955,0.00002069482,0.0002348601,0.00003261949,0.001909415],"genre_scores_gemma":[0.9907666,0.001064994,0.00734166,0.00009639328,0.000007649214,0.0000190232,0.0001362942,0.000009296403,0.0005580074],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07099131,"threshold_uncertainty_score":0.1411562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1545846977318368,"score_gpt":0.3576848173641851,"score_spread":0.2031001196323483,"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."}}