{"id":"W2756775383","doi":"","title":"A multivariate destination policy for geometallurgical variables in mineral value chains using coalition-formation clustering","year":2016,"lang":"en","type":"article","venue":"RePEc: Research Papers in Economics","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Cluster analysis; Operations research; Computer science; Set (abstract data type); Value (mathematics); Multivariate statistics; Relation (database); Variable (mathematics); Process (computing); Data mining; Industrial engineering; Engineering; Artificial intelligence; Mathematics; Machine learning","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":[0.001204147,0.0001341545,0.0002114431,0.0005981704,0.00006582077,0.000052843,0.0001583473,0.0001446584,0.00001683167],"category_scores_gemma":[0.0003183239,0.0001311137,0.00005207232,0.0001318085,0.00004714594,0.0003717591,0.00007653455,0.0001482509,0.000002138749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001442912,"about_ca_system_score_gemma":0.00007182897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001394862,"about_ca_topic_score_gemma":0.0002183416,"domain_scores_codex":[0.9986906,0.00005258943,0.0004540776,0.0002301065,0.00005882563,0.0005138022],"domain_scores_gemma":[0.9992917,0.0003419806,0.00005031739,0.0002062486,0.00003369502,0.00007610027],"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":[0.0001234594,0.00008245165,0.00160489,0.0002483447,0.00003665264,0.000006738638,0.0006229206,0.7967374,0.01951372,0.01498367,0.00002262346,0.1660171],"study_design_scores_gemma":[0.0008432186,0.0000358436,0.001857911,0.0001442079,0.00000161879,0.00001195671,0.0000666316,0.993861,0.000497642,0.001186813,0.001318074,0.0001750821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9433924,0.000009466638,0.04792093,0.0001779494,0.0001117181,0.0006226359,0.00003732446,0.0001261191,0.00760148],"genre_scores_gemma":[0.9773559,0.0003801006,0.02175594,0.00001688185,0.0001664207,0.0001541618,0.00001770934,0.00004168918,0.000111216],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1971236,"threshold_uncertainty_score":0.5346659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04624488335059315,"score_gpt":0.3191839592326957,"score_spread":0.2729390758821026,"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."}}