{"id":"W2020954181","doi":"10.1016/j.apgeochem.2012.07.022","title":"Arsenic attenuation in tailings at a former Cu–W–As mine, SW Finland","year":2012,"lang":"en","type":"article","venue":"Applied Geochemistry","topic":"Arsenic contamination and mitigation","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Basic Energy Sciences; U.S. Department of Energy; Suomen Kulttuurirahasto; National Science Foundation","keywords":"Ferrihydrite; Tailings; EMPA; Arsenopyrite; Arsenic; Electron microprobe; Arsenate; XANES; Extended X-ray absorption fine structure; Chemistry; Schwertmannite; Ferrous; Metalloid; Dissolution; Adsorption; Environmental chemistry; Geology; Mineralogy; Goethite; Absorption spectroscopy; Spectroscopy; Chalcopyrite; Metal","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001687038,0.0001066495,0.00008483669,0.00001502561,0.00006233954,0.000007431333,0.00008138432,0.00009467188,0.004406614],"category_scores_gemma":[0.00002746475,0.0001112896,0.00002855455,0.0001207962,0.00005803088,0.0001141172,0.00009416586,0.00008876706,0.001968521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002166674,"about_ca_system_score_gemma":0.000007994302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008460847,"about_ca_topic_score_gemma":0.00009546093,"domain_scores_codex":[0.9991767,0.000005952865,0.0001692011,0.0001947352,0.0001910065,0.000262376],"domain_scores_gemma":[0.9996411,0.00003165134,0.00007339258,0.0001607602,0.00000425485,0.0000888949],"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":[0.00002922954,0.00009776912,0.1440141,0.00001548237,0.000004953137,0.000001408944,0.001448124,0.00002935878,0.8320011,0.000154245,0.005234569,0.01696963],"study_design_scores_gemma":[0.0017706,0.0000153762,0.2417049,0.0000206148,0.00002818611,0.0000354951,0.0009539948,0.0007580288,0.6936342,0.001107017,0.05939501,0.000576661],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8531049,0.0000214594,0.00006498912,0.0001517309,0.00003100059,0.0001228324,0.000001658131,0.00001700355,0.1464844],"genre_scores_gemma":[0.9905065,0.000006999036,0.0002315369,0.000269494,0.00005248594,0.00005564234,0.00007195425,0.000008364173,0.008797044],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.138367,"threshold_uncertainty_score":0.9988086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006087408623872549,"score_gpt":0.2122118925588683,"score_spread":0.2061244839349957,"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."}}