{"id":"W2018441539","doi":"10.1179/174328508x283478","title":"Recovery of zinc, gallium and indium from La Oroya zinc ferrite using Na<sub>2</sub>CO<sub>3</sub>roasting","year":2008,"lang":"en","type":"article","venue":"Mineral Processing and Extractive Metallurgy Transactions of the Institutions of Mining and Metallurgy Section C","topic":"Extraction and Separation Processes","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Department of Energy","keywords":"Roasting; Zinc ferrite; Zinc; Leaching (pedology); Gallium; Calcination; Indium; Hydrometallurgy; Metallurgy; Chemistry; Electrowinning; Precipitation; Materials science; Inorganic chemistry; Nuclear chemistry; Sulfuric acid; Electrolyte; Environmental science; Catalysis","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001501174,0.000345829,0.0002949135,0.0004324688,0.000243225,0.000371477,0.0003012424,0.000324876,0.0009587434],"category_scores_gemma":[0.000217071,0.0001315431,0.0003087175,0.0002412238,0.000204792,0.0003540195,0.0001764537,0.0003107177,0.0003810564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003836771,"about_ca_system_score_gemma":0.0004168186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004995492,"about_ca_topic_score_gemma":0.01199078,"domain_scores_codex":[0.9999213,0.000007217071,0.00000588491,0.00001471807,0.00003128338,0.00001971242],"domain_scores_gemma":[0.9999352,0.0000120899,0.00001690438,0.000009000049,0.00001831365,0.000008451321],"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":[0.0001828317,0.00003247257,0.0006216601,0.00006516237,0.000007961564,0.00008657643,0.00004657797,0.0004297215,0.9904082,0.00009256266,0.00006220951,0.007963982],"study_design_scores_gemma":[0.00001579894,0.0001977217,0.003360186,0.000005754224,0.00001670749,0.0001120762,0.00004426932,0.001607941,0.9929292,0.00004325677,0.001657449,0.000009706934],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9904997,0.0001768648,0.007681951,0.00003959152,0.000007631483,0.00004058912,0.0001089174,0.0001893049,0.001255551],"genre_scores_gemma":[0.9802228,0.0003790983,0.01373724,0.00002406732,0.000005787233,0.00002987714,0.000305574,0.00005017806,0.005245493],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004995492,"threshold_uncertainty_score":0.009932816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03479653172549298,"score_gpt":0.2543009161678619,"score_spread":0.219504384442369,"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."}}