{"id":"W2080398449","doi":"10.1016/j.mineng.2011.04.014","title":"Optimizing flotation bank performance by recovery profiling","year":2011,"lang":"en","type":"article","venue":"Minerals Engineering","topic":"Minerals Flotation and Separation Techniques","field":"Environmental Science","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Profiling (computer programming); Recovery rate; Computer science; Process engineering; Environmental science; Chemistry; Engineering; Chromatography; Operating system","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001496761,0.0001302931,0.0001023466,0.00004802685,0.00005338501,0.00002243038,0.0001111042,0.00004981089,0.001008637],"category_scores_gemma":[0.00002819369,0.0001321576,0.00002930382,0.0002002419,0.00001510006,0.0004691155,0.00003492779,0.00008282731,0.0001519732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005342525,"about_ca_system_score_gemma":0.000003209122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005508784,"about_ca_topic_score_gemma":0.000003225437,"domain_scores_codex":[0.9992293,0.00001150073,0.0002188801,0.0001969918,0.0001435675,0.0001997516],"domain_scores_gemma":[0.9997337,0.00001277319,0.0000487613,0.0001315255,0.00001080144,0.00006241676],"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":[0.000007429563,0.00002074036,0.001087621,0.00001282969,0.000004827013,0.000001081639,0.0004942908,0.02017628,0.9690088,0.000111416,0.00677264,0.00230202],"study_design_scores_gemma":[0.0001463107,0.000071335,0.001344212,0.00001944978,0.000005754799,0.000004676942,0.00001471946,0.1691881,0.8186476,0.00001542336,0.01025792,0.0002844515],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9418311,0.00005180339,0.0310804,0.00002334536,0.0001271084,0.0001816164,0.000003122208,0.0003220896,0.02637943],"genre_scores_gemma":[0.8941771,0.00006821596,0.1010153,0.0001549748,0.00004084079,0.00008097071,0.00003843188,0.0000311616,0.004392962],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1503612,"threshold_uncertainty_score":0.9999046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01488909781544864,"score_gpt":0.2002983907693243,"score_spread":0.1854092929538757,"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."}}