{"id":"W4402026797","doi":"10.1016/j.ymssp.2024.111880","title":"A data-driven dynamic method of downhole rock characterisation for the vibro-impact drilling system","year":2024,"lang":"en","type":"article","venue":"Mechanical Systems and Signal Processing","topic":"Drilling and Well Engineering","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Petroleum Technology Development Fund; Petroleum Technology Research Centre","keywords":"Drilling; Measurement while drilling; Computer science; Artificial neural network; Feature (linguistics); Multilayer perceptron; Directional drilling; Drill bit; Drill; Algorithm; Geology; Artificial intelligence; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007643853,0.0001857349,0.0003206237,0.00007303516,0.0001179873,0.0002678904,0.0002019231,0.0001049662,0.000002213116],"category_scores_gemma":[0.00001423354,0.0001232766,0.00006278463,0.0001758652,0.000006838167,0.0002472613,0.00004304402,0.0001659114,0.00000225083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007260685,"about_ca_system_score_gemma":0.00002587188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000026018,"about_ca_topic_score_gemma":0.000001568981,"domain_scores_codex":[0.9988791,0.00002886638,0.0004133577,0.0002709863,0.0001711049,0.0002365656],"domain_scores_gemma":[0.9993567,0.0002771589,0.00005436069,0.0002015548,0.0000431636,0.0000671016],"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":[0.00001033516,0.000004663204,0.000001985254,0.005718875,0.000132575,0.0000025076,0.0001954237,0.8747877,0.0670854,0.0005652925,0.00002631598,0.05146896],"study_design_scores_gemma":[0.0001048191,0.00003840603,0.000008561304,0.001588642,0.00009325734,0.00003808265,0.0001889093,0.9961032,0.0007241693,0.00003035084,0.0009287393,0.0001529226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004785221,0.00729037,0.9866399,0.00001481,0.0004384695,0.0003222521,0.0001138749,0.0003679302,0.00002713682],"genre_scores_gemma":[0.9968489,0.00004653457,0.002713557,0.000001892845,0.0002367141,0.00004174871,0.00003806033,0.00005365651,0.00001886538],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9920638,"threshold_uncertainty_score":0.5027074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02248631901470251,"score_gpt":0.2796391101939956,"score_spread":0.2571527911792931,"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."}}