{"id":"W2557350180","doi":"10.3390/min6040128","title":"Experiences and Future Challenges of Bioleaching Research in South Korea","year":2016,"lang":"en","type":"article","venue":"Minerals","topic":"Metal Extraction and Bioleaching","field":"Engineering","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"National Research Foundation of Korea; National Research Foundation","keywords":"Bioleaching; Tailings; Mineral processing; Environmental science; Mining engineering; Science Citation Index; Waste management; Citation; Engineering; Metallurgy; Computer science; Library science; Materials science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004915199,0.00006101083,0.0001065251,0.0001437743,0.00002094263,0.000008976174,0.0000611404,0.00005084679,0.00004399933],"category_scores_gemma":[0.00002165193,0.00003622271,0.0000155891,0.0000806785,0.00003978398,0.00009365145,0.00001759868,0.0000997834,0.000005056389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009841219,"about_ca_system_score_gemma":0.000003023601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001497351,"about_ca_topic_score_gemma":0.00002470924,"domain_scores_codex":[0.9994495,0.00006067404,0.0001297477,0.000107011,0.0001073962,0.0001456641],"domain_scores_gemma":[0.9997857,0.00006647829,0.00001296061,0.00008721662,0.00001232679,0.00003537906],"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":[0.000009723918,0.00001662139,0.0004980344,0.00008272047,0.000009188925,0.000004114951,0.02186466,0.000009185308,0.7350172,0.003930911,0.0001248163,0.2384328],"study_design_scores_gemma":[0.003208951,0.000441634,0.01848876,0.001767014,0.00001319774,0.00004748536,0.2474913,0.002945434,0.371742,0.002544799,0.349862,0.001447412],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9838241,0.004726436,0.00002387519,0.0005212527,0.0001334279,0.0000465381,0.00000176025,0.0000306318,0.01069198],"genre_scores_gemma":[0.9983588,0.00110914,0.0001674295,0.000005056377,0.0001774537,0.0000106657,2.858346e-7,0.000006830065,0.0001643065],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3632752,"threshold_uncertainty_score":0.1477119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0763663841984381,"score_gpt":0.3174774885885986,"score_spread":0.2411111043901605,"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."}}