{"id":"W4384154846","doi":"10.21203/rs.3.rs-2918058/v1","title":"Species invasion alters fish mercury biomagnification rates","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Ministry of Natural Resources and Forestry; Queen's University; Lakehead University","funders":"Fisheries and Oceans Canada; Queen's University; Lakehead University; Ontario Ministry of Natural Resources and Forestry; University of Windsor; Ontario Federation of Anglers and Hunters; Ministry of Natural Resources; U.S. Department of Energy","keywords":"Biomagnification; Mercury (programming language); Fish <Actinopterygii>; Environmental science; Fishery; Ecology; Environmental chemistry; Chemistry; Biology; Bioaccumulation; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003535881,0.0002479158,0.0002317821,0.0005435212,0.0002404837,0.0008545789,0.000192509,0.0005558176,0.009257209],"category_scores_gemma":[0.001269138,0.0002805491,0.000468508,0.0003812772,0.0004547811,0.0006349125,0.0005469066,0.0004728103,0.0008708278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003804991,"about_ca_system_score_gemma":0.0002122756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004342565,"about_ca_topic_score_gemma":0.006545553,"domain_scores_codex":[0.9998021,0.00003742448,0.000008482404,0.00008369406,0.00002854886,0.00003982729],"domain_scores_gemma":[0.9992071,0.0002787895,0.0002162834,0.00008132376,0.00006812374,0.0001483519],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00579201,0.0004710171,0.338463,0.000164096,0.0005376356,0.0004931697,0.000761137,0.00126168,0.6332731,0.0007484082,0.001165713,0.01686909],"study_design_scores_gemma":[0.00003107723,0.0005524185,0.966856,0.000009599672,0.0001926306,0.000451895,0.0006904541,0.001937888,0.02668812,0.0004927568,0.002076378,0.00002078867],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982232,0.00009180009,0.0001441634,0.00003957486,0.000008597439,9.31071e-7,0.0001026869,0.0000182918,0.001370744],"genre_scores_gemma":[0.9971694,0.00007803438,0.0001998536,0.00003684003,0.000002816686,0.000002073498,0.0001420091,0.00002655072,0.002342423],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009257209,"threshold_uncertainty_score":0.03096843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1883120713210831,"score_gpt":0.4114348564686411,"score_spread":0.223122785147558,"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."}}