{"id":"W2285536374","doi":"10.1504/ijps.2015.072107","title":"Mine production scheduling for poly-metallic mineral deposits: extension to multiple processes","year":2015,"lang":"en","type":"article","venue":"International Journal of Planning and Scheduling","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Heuristics; Scheduling (production processes); Block (permutation group theory); Computer science; Sequence (biology); Mathematical optimization; Extension (predicate logic); Mathematics; Chemistry","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":[0.0003871078,0.0001061967,0.0001631432,0.0002084991,0.00003642486,0.00008875596,0.0001419359,0.00005046547,8.551051e-7],"category_scores_gemma":[0.0007861962,0.0001017969,0.00003866755,0.00006074681,0.000009251588,0.0002552072,0.00002641236,0.0001215364,0.000001032647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006139703,"about_ca_system_score_gemma":0.00003817656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005496536,"about_ca_topic_score_gemma":0.000005917542,"domain_scores_codex":[0.9992631,0.000006098487,0.0003431407,0.0001155136,0.000142129,0.0001300061],"domain_scores_gemma":[0.9992071,0.00006269824,0.0001129502,0.00005280974,0.0004400191,0.0001244619],"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":[0.0004146673,0.00004771246,0.01657128,0.0001131474,0.0002210655,0.00003564552,0.002809482,0.923986,0.04430456,0.00006878399,0.001046157,0.01038156],"study_design_scores_gemma":[0.003529199,0.0009772719,0.003370964,0.00328472,0.000155341,0.002404477,0.003319024,0.858944,0.1071756,0.001594005,0.01416784,0.001077535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8872685,0.001681635,0.1095547,0.0003142901,0.001026192,0.00007220468,0.000003605241,0.00005283425,0.00002603409],"genre_scores_gemma":[0.7728031,0.00003205514,0.2263512,0.00004529444,0.0007174682,0.000004669285,0.000005765224,0.00001695605,0.00002340087],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1167965,"threshold_uncertainty_score":0.4151157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04702184344644901,"score_gpt":0.2886992622629689,"score_spread":0.2416774188165199,"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."}}