{"id":"W2488819271","doi":"10.1016/j.apgeochem.2016.07.013","title":"Subsurface variations in arsenic mineralogy and geochemistry following long-term weathering of gold mine tailings","year":2016,"lang":"en","type":"article","venue":"Applied Geochemistry","topic":"Arsenic contamination and mitigation","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"Geological Survey of Canada; Natural Resources Canada; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Natural Resources Canada; U.S. Department of Energy","keywords":"Tailings; Arsenopyrite; Weathering; Geology; Dissolution; Schwertmannite; Authigenic; Gangue; Arsenic; Pyrite; Sorption; Environmental chemistry; Geochemistry; Mineralogy; Precipitation; Silicate minerals; Silicate; Chemistry; Goethite; Sedimentary rock; Adsorption; Copper; Chalcopyrite","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":[],"consensus_categories":[],"category_scores_codex":[0.0001552587,0.0001308183,0.0001555214,0.00001730593,0.00002981846,0.000007021157,0.0001168616,0.0001120211,0.000572184],"category_scores_gemma":[0.00003686198,0.0001200822,0.00004176233,0.0001189,0.0001049967,0.00007380482,0.0001146056,0.00007269361,0.00001740452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008415569,"about_ca_system_score_gemma":0.00001380848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005893204,"about_ca_topic_score_gemma":0.00008460319,"domain_scores_codex":[0.9990721,0.000007482325,0.0002598884,0.0003102895,0.0001480804,0.0002021139],"domain_scores_gemma":[0.9995263,0.00008008008,0.0001073713,0.0002137954,0.000006523349,0.00006590068],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000007206756,0.00003283605,0.07204369,0.00001880128,0.00000963708,0.000003869728,0.0001723282,0.000006300989,0.9188877,0.00003184593,0.00003834902,0.008747463],"study_design_scores_gemma":[0.001251896,0.000008165062,0.1077852,0.00006712401,0.000028492,0.00001121625,0.0001080072,0.0001559419,0.8897688,0.0004138438,0.0001507974,0.0002504256],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9865897,0.00004930392,0.0005068302,0.0001869309,0.00002327169,0.0001038144,0.000005252652,0.0000227743,0.01251215],"genre_scores_gemma":[0.9972197,0.00002344083,0.000525713,0.00002584556,0.00001748664,0.00002337682,0.00001831953,0.00001033252,0.002135812],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03574156,"threshold_uncertainty_score":0.6265014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005111412358704632,"score_gpt":0.2043346292746203,"score_spread":0.1992232169159157,"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."}}