{"id":"W2040496500","doi":"10.1016/j.hydromet.2006.09.001","title":"Modelling zinc heap bioleaching","year":2006,"lang":"en","type":"article","venue":"Hydrometallurgy","topic":"Metal Extraction and Bioleaching","field":"Engineering","cited_by":92,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Chemistry; Bioleaching; Heap (data structure); Heap leaching; Hydrometallurgy; Zinc; Process engineering; Metallurgy; Copper; Algorithm; Engineering; Computer 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":[0.0002021398,0.0001898726,0.0002028481,0.0001744394,0.0001018095,0.00007013664,0.0001243801,0.00008807019,0.000306556],"category_scores_gemma":[0.000004694175,0.0001790888,0.000123186,0.00020947,0.0000193323,0.0002201442,0.00001964349,0.0002569389,0.0004217679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004511861,"about_ca_system_score_gemma":0.000005227853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002437102,"about_ca_topic_score_gemma":0.00001533902,"domain_scores_codex":[0.999002,0.00002715319,0.0002896444,0.00019559,0.0001722738,0.0003133836],"domain_scores_gemma":[0.9996544,0.0000313531,0.00003087632,0.0001980024,0.00001146751,0.0000739563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001902671,0.00002499687,0.00001727988,0.00002277567,0.00002388711,0.000009204829,0.00001832863,0.9522367,0.03973719,0.0038281,0.0002523928,0.003827221],"study_design_scores_gemma":[0.0001289744,0.000009073548,0.00004234043,0.00001506937,0.00001171083,0.00002839788,0.00001081452,0.8601164,0.00240439,0.0007557402,0.1362499,0.0002271338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5351589,0.000729189,0.325081,0.00004667174,0.0006569627,0.00009150248,0.000003083916,0.0009647695,0.1372679],"genre_scores_gemma":[0.9927517,0.0000437743,0.00519496,0.00004203936,0.0002629592,0.000006870733,0.00003003922,0.0000449285,0.001622782],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4575928,"threshold_uncertainty_score":0.7303031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01307063475544587,"score_gpt":0.1955655725941194,"score_spread":0.1824949378386736,"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."}}