{"id":"W4293221244","doi":"10.3390/mining2030028","title":"Optimum Fleet Selection Using Machine Learning Algorithms—Case Study: Zenouz Kaolin Mine","year":2022,"lang":"en","type":"article","venue":"Mining","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Loader; Truck; Excavator; Decision tree; Boosting (machine learning); Gradient boosting; Computer science; Algorithm; Selection (genetic algorithm); Engineering; Random forest; Machine learning; Automotive engineering","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":[0.0004946669,0.0001892746,0.0002305431,0.0002000242,0.0005079978,0.00005568265,0.0001308967,0.00004548148,0.0003840479],"category_scores_gemma":[0.00001479877,0.0002439316,0.00005307093,0.0002625484,0.000009290064,0.0001115753,0.0001752431,0.0004309428,0.000005026889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002575182,"about_ca_system_score_gemma":0.00001593586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004440078,"about_ca_topic_score_gemma":0.00007375731,"domain_scores_codex":[0.9989388,0.00005914415,0.0003062464,0.000256923,0.0001097591,0.0003291313],"domain_scores_gemma":[0.9996588,0.00003924076,0.00006427729,0.000156901,0.00001574402,0.00006504518],"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":[0.00001083949,0.00005580654,0.00699209,0.00001875448,0.00007406338,0.0006286657,0.00436434,0.9711547,0.001653112,0.000005325253,0.0002994879,0.01474283],"study_design_scores_gemma":[0.0003301999,0.0002657652,0.00002412109,0.000007317502,0.00003241125,0.001920082,0.004877084,0.9852659,0.0004719957,0.000004840753,0.006529835,0.0002705176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9914809,0.0001543379,0.006480505,0.00001007483,0.0003146686,0.0001889286,0.000009721187,0.0008023225,0.000558573],"genre_scores_gemma":[0.9597805,0.000006882604,0.03964223,0.00001579442,0.0001977481,0.00004800795,0.00001798056,0.00008915846,0.0002016928],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03316173,"threshold_uncertainty_score":0.9947244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02982554798695608,"score_gpt":0.2513712618881466,"score_spread":0.2215457139011905,"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."}}