{"id":"W4407302063","doi":"10.1016/j.hydromet.2025.106453","title":"Recovery of gold and iron oxide from pyrite cinder using reduction roasting, grinding, thiosulfate leaching in the presence of additives and magnetic separation","year":2025,"lang":"en","type":"article","venue":"Hydrometallurgy","topic":"Metal Extraction and Bioleaching","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Hubei Provincial Key Laboratory for Efficient Utilization and Agglomeration of Metallurgic Mineral Resources; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Chemistry; Cinder; Roasting; Pyrite; Leaching (pedology); Grinding; Magnetic separation; Metallurgy; Thiosulfate; Iron oxide; Oxide; Inorganic chemistry; Mineralogy; Sulfur; Geology; Coal","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002134611,0.0003181469,0.0002444206,0.0002243635,0.0002035521,0.0003175757,0.0003830995,0.0003685025,0.0009025842],"category_scores_gemma":[0.0002732388,0.0001708047,0.0002337854,0.0001715999,0.0002178516,0.0002503536,0.0002217955,0.0003925983,0.0002278672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003459194,"about_ca_system_score_gemma":0.000454871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004561299,"about_ca_topic_score_gemma":0.01534312,"domain_scores_codex":[0.9998662,0.000009832616,0.000008266898,0.00003648977,0.00004144568,0.00003774483],"domain_scores_gemma":[0.9999214,0.00001928184,0.00001217966,0.000010887,0.00002473336,0.00001153453],"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":[0.00006204748,0.0000125169,0.00009834471,0.00002371055,0.0000034646,0.00003676377,0.00002683154,0.00008539417,0.9978232,0.00003103677,0.000020336,0.001776289],"study_design_scores_gemma":[0.000003835576,0.00008346339,0.0008044617,0.000001912454,0.00000512615,0.00003090167,0.00001635114,0.0004236776,0.9979751,0.00001501321,0.0006370605,0.000003233861],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931623,0.0003119033,0.004742655,0.00007072416,0.00001502978,0.00002816375,0.00009598281,0.00007634646,0.00149689],"genre_scores_gemma":[0.9819233,0.0003087762,0.008341647,0.00004032826,0.000004213694,0.0000156232,0.0003261862,0.00004352348,0.008996346],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004561299,"threshold_uncertainty_score":0.009069502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01373824268445423,"score_gpt":0.2485104712135471,"score_spread":0.2347722285290929,"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."}}