{"id":"W3027332273","doi":"10.3390/met10050669","title":"Fe(III) Precipitation and Copper Loss from Sulphate-Chloride Solutions at 150 °C: A Statistical Approach","year":2020,"lang":"en","type":"article","venue":"Metals","topic":"Mine drainage and remediation techniques","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Precipitation; Copper; Supersaturation; Hematite; Seeding; Chloride; Nucleation; Chemistry; Tailings; Magnetite; Inorganic chemistry; Metal; Particle size; Metallurgy; Materials science; Mineralogy; Thermodynamics; Meteorology; Physical chemistry","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001651272,0.0001005059,0.0001453127,0.00001434009,0.0001205686,0.00002873983,0.00008913803,0.00005307141,0.001478435],"category_scores_gemma":[0.0001091614,0.00008453497,0.0000289665,0.00009996777,0.0001492822,0.0001801219,0.0001754128,0.00006873642,0.0003713113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005391538,"about_ca_system_score_gemma":0.000004548568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001910836,"about_ca_topic_score_gemma":0.00003639987,"domain_scores_codex":[0.9990828,0.00006100155,0.0001820714,0.000287372,0.0002196422,0.0001671333],"domain_scores_gemma":[0.999603,0.00009027604,0.00005321667,0.0001086789,0.000006492902,0.0001382814],"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":[0.0004281343,0.0006195523,0.01505337,0.0001148207,0.0002591639,0.0000590548,0.02514598,0.001240603,0.6263689,0.01090445,0.2958383,0.02396762],"study_design_scores_gemma":[0.004075103,0.0006560622,0.09404457,0.00003452312,0.0005785108,0.00003418155,0.001529309,0.1083943,0.5253281,0.02347909,0.2397259,0.002120325],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7987203,0.000250322,0.1888901,0.003209137,0.00006394424,0.0005427454,0.0001926695,0.000178166,0.007952579],"genre_scores_gemma":[0.9816223,0.00005412783,0.0167527,0.0007173418,0.00006784149,0.00004185286,0.0001590031,0.00001034266,0.0005745387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.182902,"threshold_uncertainty_score":0.9994344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02734345884689413,"score_gpt":0.2455856327886214,"score_spread":0.2182421739417273,"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."}}