{"id":"W4282840164","doi":"10.3390/en15124288","title":"Applying Machine Learning to Predict the Rate of Penetration for Geothermal Drilling Located in the Utah FORGE Site","year":2022,"lang":"en","type":"article","venue":"Energies","topic":"Drilling and Well Engineering","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Drilling; Rate of penetration; Artificial neural network; Random forest; Geothermal gradient; Machine learning; Hyperparameter; Preprocessor; Data pre-processing; Computer science; Penetration rate; Artificial intelligence; Data mining; Engineering; Petroleum engineering; Geology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005419563,0.0004779732,0.0002212077,0.0007440184,0.0001940377,0.0003266702,0.0004077367,0.0005209739,0.0006431046],"category_scores_gemma":[0.002127019,0.0002425927,0.0003511826,0.0005775999,0.0001758009,0.0005728111,0.0002087555,0.0005505161,0.000163353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000563239,"about_ca_system_score_gemma":0.0006009288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02843332,"about_ca_topic_score_gemma":0.04691759,"domain_scores_codex":[0.9998727,0.00002423955,0.00001016404,0.00003328304,0.00003971396,0.00001998182],"domain_scores_gemma":[0.9993387,0.0003303399,0.00008549621,0.00003932749,0.0001843075,0.00002181644],"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":[0.00008283304,0.000120374,0.04698185,0.00003629252,0.00002965795,0.0001495499,0.00005717603,0.9092218,0.004619492,0.0002981524,0.0006565346,0.03774626],"study_design_scores_gemma":[0.000002340792,0.00002247644,0.00544035,0.000003163117,0.000003033173,0.000007190001,0.00001931964,0.9926515,0.001626717,0.0001088892,0.0001098005,0.000005243498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9562419,0.00007152364,0.04124805,0.0001307515,0.00001760584,0.00004221956,0.0004962029,0.0006474613,0.001104374],"genre_scores_gemma":[0.9852955,0.00003311961,0.01375072,0.000009750982,0.000002255852,0.00002145307,0.0004289285,0.00001427696,0.0004439753],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02843332,"threshold_uncertainty_score":0.05653566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007606805115766618,"score_gpt":0.1908804147961874,"score_spread":0.1832736096804208,"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."}}