{"id":"W961312862","doi":"10.1007/s00128-015-1588-3","title":"Mercury Contamination in an Indicator Fish Species from Andean Amazonian Rivers Affected by Petroleum Extraction","year":2015,"lang":"en","type":"article","venue":"Bulletin of Environmental Contamination and Toxicology","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal; McGill University","funders":"Associazione Umbra per la lotta Contro il Cancro onlus; Fonds Québécois de la Recherche sur la Nature et les Technologies; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; International Development Research Centre","keywords":"Amazon rainforest; Bioindicator; Mercury (programming language); Deforestation (computer science); Environmental science; Drainage basin; Amazonian; Amazon basin; Ecotoxicology; Fishery; Contamination; Petroleum; Ecology; Geography; Biology","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":[0.00009632554,0.0002381202,0.0001531706,0.0007704789,0.0008619817,0.0004408581,0.0002072045,0.0002803483,0.0005272444],"category_scores_gemma":[0.0001866542,0.0001585983,0.0001750185,0.0006449153,0.0004032664,0.0001688149,0.0003966773,0.0001589683,0.00009428883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005323992,"about_ca_system_score_gemma":0.000389236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05699945,"about_ca_topic_score_gemma":0.1040473,"domain_scores_codex":[0.9999164,0.000008875505,0.000007226506,0.00002598005,0.00002203816,0.00001939162],"domain_scores_gemma":[0.9998682,0.00001260003,0.00004823923,0.000005775365,0.00004433157,0.00002084745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003302681,0.00006911246,0.8821135,0.00004802195,0.00004955754,0.000721212,0.003008878,0.0001209884,0.1095642,0.00006211901,0.00005739965,0.003854844],"study_design_scores_gemma":[0.000003791773,0.0001233496,0.9937509,0.000002719836,0.00002126336,0.0002824029,0.001505282,0.0001377293,0.00385958,0.00001444027,0.0002947518,0.000003733606],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999714,0.000008999114,0.00001964856,0.000004168072,3.402266e-7,0.000002008081,0.00004107813,7.956165e-7,0.0002089488],"genre_scores_gemma":[0.9993663,0.00002414364,0.00008608866,0.000008124506,5.597719e-7,0.000003363424,0.00007273061,0.000001132669,0.0004374504],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05699945,"threshold_uncertainty_score":0.1133354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01305601381688812,"score_gpt":0.2377840601464553,"score_spread":0.2247280463295671,"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."}}