{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004286646,0.000505079,0.000789942,0.001648425,0.0003814122,0.0007164106,0.0007144075,0.0004746185,0.001005615],"category_scores_gemma":[0.00637309,0.0002350957,0.001333995,0.001894044,0.0007274495,0.0003851266,0.0004911543,0.0009533837,0.0002357641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000578356,"about_ca_system_score_gemma":0.0008746482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001932314,"about_ca_topic_score_gemma":0.001669728,"domain_scores_codex":[0.9976445,0.0007652644,0.0002190724,0.0004495481,0.000782493,0.000139115],"domain_scores_gemma":[0.9936466,0.004492747,0.0007424307,0.0004873932,0.0005674451,0.00006329206],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.005522532,0.002679059,0.1953668,0.001719117,0.00306115,0.0005871071,0.001748252,0.1047815,0.3771406,0.009466963,0.00153316,0.2963937],"study_design_scores_gemma":[0.0001083484,0.009016197,0.1992785,0.00004830968,0.0007606365,0.0005612607,0.0008895697,0.529607,0.2479116,0.003979626,0.007655542,0.0001835328],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8130674,0.0004560892,0.1826504,0.00007715738,0.00003392816,0.0004790062,0.001327267,0.0007146301,0.001194019],"genre_scores_gemma":[0.9073567,0.0002062385,0.0886955,0.00004010917,0.0000221923,0.001010724,0.001467253,0.00007888018,0.00112247],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004286646,"threshold_uncertainty_score":0.02267021,"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."}}