{"id":"W2911165846","doi":"10.1080/16843703.2018.1564485","title":"Cost optimization of drilling operations in open-pit mines through parameter tuning","year":2019,"lang":"en","type":"article","venue":"Quality Technology & Quantitative Management","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Drilling; Drill; Computer science; Penetration rate; Petroleum engineering; Engineering; Mechanical 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009055889,0.0005633128,0.0005364292,0.0005934516,0.0002151461,0.0006756532,0.0004970815,0.0006180551,0.0006828194],"category_scores_gemma":[0.001662581,0.0003122602,0.0003856619,0.0003599857,0.000331393,0.0005417265,0.0003505955,0.000355432,0.0001120961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005335317,"about_ca_system_score_gemma":0.0009395508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001618765,"about_ca_topic_score_gemma":0.002870911,"domain_scores_codex":[0.9996465,0.0001101438,0.00001720204,0.00005689255,0.000115715,0.00005349527],"domain_scores_gemma":[0.9991634,0.0004602359,0.0001958096,0.00003457952,0.0001163222,0.00002962893],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002452508,0.0002358293,0.002597625,0.0002193666,0.00003001016,0.00006418685,0.0000416338,0.9428275,0.01460515,0.000640428,0.0001359253,0.03835713],"study_design_scores_gemma":[0.00004989354,0.0009207592,0.003587414,0.0000169334,0.00003748368,0.00005290434,0.00007424409,0.9846821,0.009294906,0.0009414647,0.0003256889,0.00001632594],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7047608,0.0004681806,0.2908349,0.00009828204,0.00001795562,0.0001762943,0.00009770459,0.0001690223,0.003376849],"genre_scores_gemma":[0.9580315,0.00008471618,0.04141074,0.00001123205,0.000002629361,0.00007058403,0.00004127402,0.000012125,0.0003353448],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001618765,"threshold_uncertainty_score":0.004789293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0956457242883104,"score_gpt":0.3602658595963494,"score_spread":0.264620135308039,"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."}}