{"id":"W2058871943","doi":"10.1073/pnas.0609798104","title":"Forest fire increases mercury accumulation by fishes via food web restructuring and increased mercury inputs","year":2006,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":174,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; University of Alberta","funders":"","keywords":"Mercury (programming language); Food web; Trophic level; Environmental science; Trout; Rainbow trout; Ecology; Food chain; STREAMS; Environmental chemistry; Fishery; Chemistry; Biology; Fish <Actinopterygii>","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":[0.0001095787,0.0001670841,0.0001144987,0.0002486719,0.0002895603,0.0002214671,0.00009075712,0.000205044,0.001002633],"category_scores_gemma":[0.0001399508,0.0001369756,0.0002006978,0.0001349328,0.0002556723,0.0001340164,0.0002432407,0.0002052999,0.00007636994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004057826,"about_ca_system_score_gemma":0.0002373457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00660282,"about_ca_topic_score_gemma":0.0159112,"domain_scores_codex":[0.9999356,0.000007904255,0.000003806576,0.00001329429,0.00002005425,0.00001932114],"domain_scores_gemma":[0.9998927,0.00001173891,0.00004683489,0.000007184583,0.00001392612,0.00002757564],"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":[0.001057465,0.0001867431,0.2717407,0.0001180566,0.0001858066,0.0006358231,0.0001876679,0.000526295,0.704847,0.00007303842,0.0002063436,0.0202351],"study_design_scores_gemma":[0.00001299862,0.0003296121,0.9813117,0.000004819551,0.0000335841,0.0003896364,0.0001282315,0.0004531696,0.01695284,0.0000718865,0.0003065761,0.000004975614],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995284,0.00008278458,0.0001082897,0.0000171821,0.000001783472,0.000002322397,0.00001945219,0.000008810259,0.0002310395],"genre_scores_gemma":[0.9991298,0.0001595321,0.0003352573,0.00002819102,0.000003722721,0.000004047397,0.00005162765,0.000001940996,0.0002860318],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00660282,"threshold_uncertainty_score":0.01312876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02774018705919192,"score_gpt":0.2754146854497094,"score_spread":0.2476744983905174,"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."}}