{"id":"W2786291776","doi":"10.1007/s13280-017-1006-7","title":"Challenges and opportunities for managing aquatic mercury pollution in altered landscapes","year":2018,"lang":"en","type":"article","venue":"AMBIO","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":253,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"National Institute of Environmental Health Sciences; Vetenskapsrådet; U.S. Department of Energy","keywords":"Mercury (programming language); Environmental science; Wildlife; Environmental remediation; Bioaccumulation; Urbanization; Environmental planning; Biota; Wetland; Pollution; Methylmercury; Gold mining; Watershed; Tailings; Anthropocene; Environmental resource management; Environmental protection; Ecology; Environmental chemistry; Contamination; Chemistry; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001690298,0.00005925306,0.00008050517,0.00003008423,0.00007115678,0.00001012726,0.00003708384,0.00001856401,0.00008208905],"category_scores_gemma":[0.00001765632,0.00005201085,0.0000117261,0.00002595888,0.0001033026,0.0001327318,0.00004175442,0.00001899733,0.00002972076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001799636,"about_ca_system_score_gemma":0.000002175721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008420563,"about_ca_topic_score_gemma":0.001111076,"domain_scores_codex":[0.9995851,0.00001880893,0.00008666611,0.0001144495,0.00006348337,0.0001315176],"domain_scores_gemma":[0.999846,0.00002603625,0.00002735226,0.0000642322,0.000002607624,0.00003371216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00004610883,0.00005490272,0.01020868,0.00005397962,0.00002943934,0.000003081393,0.01306308,0.000003707162,0.005663706,0.003137711,0.00860012,0.9591355],"study_design_scores_gemma":[0.00200465,0.0007149694,0.7934571,0.0001587398,0.00006068807,0.00002025197,0.02104118,0.003375754,0.004539207,0.03345391,0.140488,0.0006855811],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9546931,0.001672428,0.0002214992,0.01087592,0.000127661,0.0001976916,0.000004321483,0.00002713217,0.03218024],"genre_scores_gemma":[0.9970637,0.001954286,0.0001649846,0.000464223,0.00005294883,0.0000169444,0.000003536741,0.000004456791,0.0002749196],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9584499,"threshold_uncertainty_score":0.2120941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08943360913584743,"score_gpt":0.2915631144461513,"score_spread":0.2021295053103039,"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."}}