{"id":"W3175509498","doi":"10.1016/j.petrol.2021.109132","title":"Experimental measurement and modeling of water-based drilling mud density using adaptive boosting decision tree, support vector machine, and K-nearest neighbors: A case study from the South Pars gas field","year":2021,"lang":"en","type":"article","venue":"Journal of Petroleum Science and Engineering","topic":"Drilling and Well Engineering","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Polytechnique Montréal","funders":"Academy of Neonatal Nursing","keywords":"Drilling fluid; Support vector machine; Decision tree; Rate of penetration; Drilling; Petroleum engineering; Geology; Mathematics; Algorithm; Statistics; Soil science; Artificial intelligence; Computer science; Engineering; Mechanical 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":[0.001071171,0.0002014317,0.000314142,0.0001672468,0.000192769,0.0001413756,0.0001121328,0.00004289298,0.000002062645],"category_scores_gemma":[0.000178542,0.0001483866,0.00004945958,0.0001819793,0.00004289653,0.0002949341,0.00008135049,0.0002756707,7.491516e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000988626,"about_ca_system_score_gemma":0.00006679277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001560103,"about_ca_topic_score_gemma":0.00002900803,"domain_scores_codex":[0.9984878,0.00001754324,0.0004325876,0.0002116563,0.0005597566,0.0002906387],"domain_scores_gemma":[0.9992905,0.0001445418,0.00006566323,0.0001421569,0.0001924935,0.0001646741],"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":[0.00001693509,0.00001947834,0.001985149,0.00002147912,0.00003622143,0.0003187153,0.002221743,0.8608112,0.1338691,0.000001423,4.609803e-7,0.0006981199],"study_design_scores_gemma":[0.0005510692,0.000154273,0.0003897452,0.0002686648,0.00006096937,0.0005288238,0.00425044,0.9472998,0.04633221,0.000002936123,0.000002123373,0.0001589816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8281662,0.001244399,0.1702675,0.00001029336,0.0002351588,0.00004817581,0.000002422204,0.00002039819,0.000005483918],"genre_scores_gemma":[0.9934847,0.00002672828,0.006363715,0.00000878998,0.00009253952,0.000001150783,2.619547e-7,0.00002188866,2.183555e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1653185,"threshold_uncertainty_score":0.6051029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03224653867717357,"score_gpt":0.2315656818433237,"score_spread":0.1993191431661502,"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."}}