{"id":"W2164942693","doi":"10.1144/geochem2012-186","title":"Biogeochemical controls on metal mobility: modeling a Cu-Zn VMS deposit in column flow-through studies","year":2013,"lang":"en","type":"article","venue":"Geochemistry Exploration Environment Analysis","topic":"Mine drainage and remediation techniques","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Biogeochemical cycle; Column (typography); Flow (mathematics); Metal; Geology; Geochemistry; Environmental science; Chemistry; Environmental chemistry; Computer science; Materials science; Metallurgy; Physics; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002963358,0.0002969969,0.0004722451,0.00009046542,0.0001274743,0.00005143448,0.0002277958,0.000159539,0.002908265],"category_scores_gemma":[0.0000917811,0.0002771844,0.0002528297,0.0005459141,0.0001747129,0.000625563,0.0001778118,0.0001929033,0.0004407413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00043234,"about_ca_system_score_gemma":0.000005040666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005323632,"about_ca_topic_score_gemma":0.000116604,"domain_scores_codex":[0.9976614,0.00008278664,0.0006185752,0.0007107722,0.0005758457,0.0003506746],"domain_scores_gemma":[0.9991041,0.00006895552,0.000169358,0.0005401272,0.00001528374,0.0001021324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004477191,0.0009330391,0.01485675,0.00003644216,0.0007971071,0.00001479901,0.002245954,0.4483061,0.5289341,0.00001252855,0.002205291,0.001613085],"study_design_scores_gemma":[0.0006351889,0.00007511081,0.0003340145,0.00002458936,0.0005107058,0.000001209004,0.001950958,0.7042556,0.2863391,0.00469294,0.0005986755,0.0005818862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9669195,0.0001905657,0.02978652,0.00108767,0.00001730386,0.0007214067,0.00002201513,0.00008723882,0.001167755],"genre_scores_gemma":[0.9935063,0.0004413999,0.003842124,0.0002967481,0.00004927667,0.001082885,0.0004309606,0.00001520639,0.0003350911],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2559495,"threshold_uncertainty_score":0.9999681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02220087918486996,"score_gpt":0.245927002946795,"score_spread":0.223726123761925,"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."}}