{"id":"W2141769977","doi":"10.1071/aseg2015ab042","title":"Logging during diamond drilling - Autonomous logging integrated into the Bottom Hole Assembly","year":2015,"lang":"en","type":"article","venue":"ASEG Extended Abstracts","topic":"Tunneling and Rock Mechanics","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Drilling; Logging; Borehole; Measurement while drilling; Scientific drilling; Petroleum engineering; Mud logging; Diamond; Geology; Drilling fluid; Engineering; Mechanical engineering; Geotechnical engineering; Materials science","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006239565,0.0003794305,0.0003079728,0.000167857,0.0003478882,0.0001831088,0.0003971499,0.0002102146,0.00001052672],"category_scores_gemma":[0.000179013,0.0003013851,0.0001111363,0.0002444357,0.00003301182,0.0003233578,0.00009489103,0.0008850052,0.000173155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002672145,"about_ca_system_score_gemma":0.0001023943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002011053,"about_ca_topic_score_gemma":0.0000600692,"domain_scores_codex":[0.9980294,0.00004962675,0.0005118428,0.0003767794,0.0003360477,0.0006962884],"domain_scores_gemma":[0.9988416,0.0001293307,0.0001113982,0.0005141941,0.00009400464,0.0003094881],"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":[0.00002988298,0.00008573293,0.00003479196,0.0001512394,0.0001647559,0.0003325128,0.006597055,0.8996601,0.03017804,0.0002397129,0.001431615,0.06109464],"study_design_scores_gemma":[0.002081757,0.0001035107,0.008752618,0.0005514295,0.0001654062,0.0002062779,0.007676146,0.6495057,0.3090315,0.005215275,0.01487884,0.001831559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9873813,0.00132277,0.006374021,0.000204643,0.001567273,0.0001958676,0.000006743595,0.001411477,0.001535902],"genre_scores_gemma":[0.9967575,0.00007352821,0.002266439,0.00007831074,0.0003639164,0.00002226589,0.00002905545,0.0001086845,0.0003002736],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2788535,"threshold_uncertainty_score":0.9999439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01398860149666897,"score_gpt":0.2252863091395826,"score_spread":0.2112977076429136,"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."}}