{"id":"W4323519865","doi":"10.15372/ftprpi20230108","title":"A Novel Rate of Penetration Prediction Model for Large Diameter Drilling: An Approach Based on TBM and RBM Applications","year":2023,"lang":"ru","type":"article","venue":"Физико-технические проблемы разработки полезных ископаемых","topic":"Tunneling and Rock Mechanics","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Rate of penetration; Penetration rate; Drilling; Penetration (warfare); Petroleum engineering; Computer science; Materials science; Geology; Engineering; Mechanical engineering; Operations research","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005397738,0.0006854603,0.001217602,0.0005373921,0.000417933,0.0007931541,0.001511337,0.001379363,0.001566158],"category_scores_gemma":[0.001196616,0.0005591175,0.0008631262,0.0006723541,0.0003359439,0.00109644,0.0005449197,0.0009443891,0.000415697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006679094,"about_ca_system_score_gemma":0.001162197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01723358,"about_ca_topic_score_gemma":0.008301803,"domain_scores_codex":[0.9997951,0.00003758453,0.00001391964,0.00006852911,0.00005655687,0.00002821713],"domain_scores_gemma":[0.9996799,0.0001286119,0.00004792281,0.00001996079,0.0001078599,0.00001581304],"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":[0.00001987969,0.00002330105,0.0006729572,0.0000483178,0.00002061144,0.00004805155,0.00002104441,0.9739563,0.001694698,0.002533011,0.0004239041,0.02053794],"study_design_scores_gemma":[3.696429e-7,0.0000017084,0.00002646437,8.700642e-7,0.000001658001,0.000002571092,0.000001010593,0.9997213,0.00005586234,0.0001426498,0.00004430443,0.00000110937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0193509,0.0003516385,0.977864,0.0001253929,0.00005269206,0.00002759362,0.00006995865,0.0003663181,0.001791609],"genre_scores_gemma":[0.8732703,0.0007723279,0.1196221,0.0001180268,0.0000995845,0.0002126099,0.0002349896,0.0001307341,0.005539341],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01723358,"threshold_uncertainty_score":0.03426653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03575024716757898,"score_gpt":0.2459233637130523,"score_spread":0.2101731165454733,"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."}}