{"id":"W4297312265","doi":"10.1007/s10601-022-09337-w","title":"Short- and medium-term optimization of underground mine planning using constraint programming","year":2022,"lang":"en","type":"article","venue":"Constraints","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Constraint programming; Computer science; Scheduling (production processes); Integer programming; Constraint (computer-aided design); Constraint satisfaction; Automation; Mathematical optimization; Task (project management); Operations research; Industrial engineering; Stochastic programming; Engineering; Artificial intelligence; Algorithm; Mathematics; Systems engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001834681,0.0001049878,0.000164362,0.0000897768,0.00008576577,0.00002329461,0.00008133173,0.00004060216,0.0001767134],"category_scores_gemma":[0.00001014408,0.0001313189,0.00002676592,0.00007011637,0.0001720551,0.00006708739,0.00007213184,0.0001266428,1.221458e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007403781,"about_ca_system_score_gemma":0.00002590939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002704575,"about_ca_topic_score_gemma":7.451268e-7,"domain_scores_codex":[0.9993702,0.00001310301,0.0002458794,0.0001309695,0.00006908892,0.0001707235],"domain_scores_gemma":[0.9997545,0.00003378976,0.00004279008,0.0001035911,0.00001345818,0.00005180273],"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":[0.000009266014,0.00002822375,0.01030022,0.0001401389,0.00008883122,0.00003335012,0.0009214618,0.9217125,0.003194168,0.0008130423,0.00006958569,0.0626892],"study_design_scores_gemma":[0.0004284864,0.00009054711,0.0009323556,0.0001098525,0.00004891623,0.0004467765,0.002908141,0.9932825,0.0008522067,0.0001828688,0.000321881,0.0003954477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8448784,0.0001932763,0.1536311,0.0000106796,0.0001563996,0.0001638129,0.00002470745,0.0001576538,0.0007839351],"genre_scores_gemma":[0.9435108,0.00001254567,0.05638404,0.00001219287,0.00002173672,0.00001324263,0.00002125066,0.00002121851,0.000002979947],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0986324,"threshold_uncertainty_score":0.535503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03815083992490736,"score_gpt":0.2579665200586732,"score_spread":0.2198156801337658,"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."}}