{"id":"W4248991745","doi":"10.31031/amms.2017.01.000502","title":"Metals from Ores: An Introduction","year":2017,"lang":"en","type":"article","venue":"Aspects in Mining & Mineral Science","topic":"Metal Extraction and Bioleaching","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Library science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005356743,0.001385445,0.001091287,0.002999337,0.001289455,0.002548818,0.0009245644,0.002457113,0.0100767],"category_scores_gemma":[0.0006091598,0.0006792941,0.0008977178,0.002078488,0.001291148,0.003497413,0.001793287,0.002610729,0.006100866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001099856,"about_ca_system_score_gemma":0.0007502504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001791641,"about_ca_topic_score_gemma":0.002841471,"domain_scores_codex":[0.9996687,0.00004180047,0.00003872598,0.00007851511,0.0001349755,0.00003725378],"domain_scores_gemma":[0.9997349,0.00009390833,0.00003093595,0.00001431111,0.00008533449,0.00004054596],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002657821,0.0006098149,0.001192928,0.01046081,0.0001129746,0.002595214,0.0009879515,0.002436172,0.02049817,0.08268084,0.3465149,0.5316444],"study_design_scores_gemma":[0.000002945739,0.0001169822,0.0005571624,0.0005029849,0.000008049153,0.0009947903,0.0001339502,0.0001455404,0.0007705721,0.01020459,0.9865404,0.00002195901],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.004216155,0.901996,0.006711002,0.009057046,0.02043786,0.00009747154,0.0004311644,0.0001405084,0.05691276],"genre_scores_gemma":[0.01403629,0.8473912,0.005600336,0.007132323,0.02214131,0.0001372723,0.0005298114,0.0001094569,0.1029219],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0100767,"threshold_uncertainty_score":0.03370994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0227273574441328,"score_gpt":0.2886468848631029,"score_spread":0.2659195274189701,"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."}}