{"id":"W3020480229","doi":"","title":"Impacts of Wildfires on Mercury Contamination in Canada","year":2017,"lang":"en","type":"article","venue":"AGUFM","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Mercury (programming language); Contamination; Mercury contamination; Environmental science; Geography; Computer science; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004475312,0.0002772689,0.0002208433,0.0009934149,0.0030844,0.0009880239,0.0006551083,0.0003930709,0.001379857],"category_scores_gemma":[0.000771367,0.0001494811,0.0002999573,0.001655735,0.0009188167,0.0003166527,0.0005043841,0.0004432151,0.00009627568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03930687,"about_ca_system_score_gemma":0.03745203,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9969376,"about_ca_topic_score_gemma":0.9989202,"domain_scores_codex":[0.99954,0.00004778268,0.00001046366,0.00003609059,0.0001719292,0.000193803],"domain_scores_gemma":[0.999169,0.00007795542,0.00008849199,0.00002251429,0.0004927144,0.0001492931],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001363694,0.0005385836,0.8989302,0.0002215781,0.0003681331,0.0008789476,0.004933883,0.003860169,0.007525512,0.001964028,0.006043903,0.07337144],"study_design_scores_gemma":[0.00001653674,0.0000919983,0.9860129,0.00004003435,0.00006685071,0.00005797066,0.006026869,0.0009057675,0.001336037,0.0001517932,0.005275356,0.00001788609],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994904,0.0006630413,0.0000565003,0.000337264,0.000008307784,0.00001289721,0.000582422,0.000008613792,0.003426899],"genre_scores_gemma":[0.9954178,0.0009810212,0.0001408645,0.0001131343,0.000004107678,0.000006074125,0.0002832921,0.000004128524,0.003049496],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03930687,"threshold_uncertainty_score":0.2851927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01544419454604053,"score_gpt":0.25763769825338,"score_spread":0.2421935037073394,"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."}}