{"id":"W2947518691","doi":"10.15377/2409-5710.2018.05.4","title":"A Clustering Algorithm for Block-Cave Production Scheduling","year":2019,"lang":"en","type":"article","venue":"Global Journal of Earth Science and Engineering","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Scheduling (production processes); Cluster analysis; Computer science; Production schedule; Mathematical optimization; Schedule; Algorithm; Industrial engineering; Engineering; Mathematics; Artificial intelligence","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":[0.0005517428,0.0001069418,0.0001527791,0.0001440912,0.00006294883,0.0001029179,0.0001349484,0.00003785583,0.000002601897],"category_scores_gemma":[0.0001077957,0.0001018142,0.00004077786,0.0004595078,0.00003143704,0.0004592213,0.00002195386,0.0001100496,0.000003117977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004330144,"about_ca_system_score_gemma":0.00004711066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.757583e-7,"about_ca_topic_score_gemma":2.854995e-7,"domain_scores_codex":[0.9990996,0.000002321321,0.0002339298,0.0001261937,0.000281436,0.0002565713],"domain_scores_gemma":[0.9994512,0.00001521928,0.00004973887,0.00008317827,0.0002737032,0.0001269231],"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":[0.000002616642,0.000003467078,0.0001523677,0.0000334806,0.00001262392,0.000001380722,0.00005124397,0.9683394,0.005572159,0.00001913833,0.000004548239,0.02580758],"study_design_scores_gemma":[0.0002792285,0.00005469879,0.0006713418,0.0001017764,0.00001004622,0.0002454239,0.0001075093,0.9954961,0.00269949,0.00000981648,0.0002029785,0.0001215804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4484881,0.0005138647,0.5482998,0.00003620998,0.002354052,0.0001236452,0.000002809406,0.00007200311,0.0001095775],"genre_scores_gemma":[0.5312338,0.0000655554,0.4684248,0.00001257653,0.0002421996,0.000001344274,2.777625e-7,0.000009320574,0.00001011376],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.08274573,"threshold_uncertainty_score":0.4151862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007066089317860959,"score_gpt":0.2147099069752467,"score_spread":0.2076438176573857,"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."}}