{"id":"W4390966596","doi":"10.1007/s10530-023-03238-6","title":"Spiny water flea invasion alters fish mercury bioaccumulation rates","year":2024,"lang":"en","type":"article","venue":"Biological Invasions","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Queen's University; Lakehead University; Ministry of Natural Resources and Forestry; International Institute for Sustainable Development","funders":"Fisheries and Oceans Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Ministry of Natural Resources; Ontario Federation of Anglers and Hunters; Lakehead University","keywords":"Trophic level; Biology; Bioaccumulation; Ecology; Zooplankton; Foraging; Mercury (programming language); Fishery","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002762697,0.0001650773,0.0001544387,0.00004592425,0.0002650133,0.00007234016,0.0001418945,0.0001218112,0.006440297],"category_scores_gemma":[0.0001283795,0.00008774838,0.0001006272,0.0002140852,0.0002633257,0.0002332807,0.0002668441,0.0001386636,0.003895079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006919348,"about_ca_system_score_gemma":0.000006560124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008018856,"about_ca_topic_score_gemma":0.00003938604,"domain_scores_codex":[0.9988135,0.00008963745,0.0002444159,0.000365499,0.0001699983,0.0003169304],"domain_scores_gemma":[0.9995172,0.0001584511,0.00002188567,0.0001609068,0.000007857018,0.0001336749],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002423905,0.0001163772,0.04553098,0.00002150015,0.00005117036,0.0000356995,0.001139913,0.0001640164,0.8299778,0.001186652,0.1015389,0.02021277],"study_design_scores_gemma":[0.000421339,0.0004854711,0.2754056,0.0001606092,0.00006682141,0.00003905661,0.0007679957,0.001683064,0.205305,0.01043071,0.5042554,0.0009788586],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9891719,0.0001560439,0.0002868874,0.004194996,0.0004498331,0.0002188341,0.00002264992,0.0002155981,0.005283298],"genre_scores_gemma":[0.9969426,0.0003852785,0.0002707486,0.001465648,0.0001009895,0.00003048653,0.0001070472,0.000009302875,0.0006879413],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6246728,"threshold_uncertainty_score":0.9968805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2126032207480709,"score_gpt":0.3194702486391623,"score_spread":0.1068670278910914,"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."}}