{"id":"W4387410849","doi":"10.1016/j.scitotenv.2023.167643","title":"Seasonal riverine inputs may affect diet and mercury bioaccumulation in Arctic coastal zooplankton","year":2023,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"University of California, Davis; Norsk Institutt for Vannforskning; Norges Forskningsråd","keywords":"Zooplankton; Environmental science; Bioaccumulation; Arctic; Phytoplankton; Estuary; Mercury (programming language); Oceanography; Methylmercury; Permafrost; Ecology; Environmental chemistry; Nutrient; Biology; Chemistry; Geology","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":[0.0001084889,0.0001367762,0.0001086646,0.0002255086,0.0002798447,0.0003503646,0.00007122538,0.0001289999,0.0005446055],"category_scores_gemma":[0.0001824565,0.0001132303,0.00013871,0.0002460472,0.0001179592,0.0001331757,0.0001894365,0.0001044344,0.00009337583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004446844,"about_ca_system_score_gemma":0.0002656073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03338978,"about_ca_topic_score_gemma":0.08617975,"domain_scores_codex":[0.9999552,0.00001138791,0.000003246844,0.000009021248,0.000009828906,0.00001139758],"domain_scores_gemma":[0.9999177,0.00001682107,0.00002618393,0.000003173398,0.0000188604,0.00001715961],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003140199,0.00009008918,0.9203925,0.00004452755,0.0001205674,0.0001765069,0.0005911537,0.0005349015,0.06724623,0.00004708351,0.0001087064,0.01033374],"study_design_scores_gemma":[6.768884e-7,0.00002927852,0.9993572,0.000001066438,0.00000604068,0.00001107346,0.0001099284,0.00008590596,0.0003084454,0.000006105996,0.00008336438,9.168987e-7],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9997275,0.00003944452,0.00001794833,0.000003907727,6.408886e-7,7.301024e-7,0.00004915622,9.637114e-7,0.0001597251],"genre_scores_gemma":[0.9992573,0.0001338375,0.00008335349,0.00001509199,0.000001634172,0.00000339895,0.0001419773,0.000001398266,0.0003618693],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03338978,"threshold_uncertainty_score":0.06639087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01832732811649725,"score_gpt":0.2557331126906552,"score_spread":0.2374057845741579,"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."}}