{"id":"W4387209777","doi":"10.1016/j.geoen.2023.212381","title":"A novel well-logging data generation model integrated with random forests and adaptive domain clustering algorithms","year":2023,"lang":"en","type":"article","venue":"Geoenergy Science and Engineering","topic":"Hydrocarbon exploration and reservoir analysis","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Ministry of Science and Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Cluster analysis; Mean squared error; Mean absolute percentage error; Random forest; Computer science; Algorithm; Artificial neural network; Robustness (evolution); Convolutional neural network; Data mining; Pattern recognition (psychology); Statistics; Artificial intelligence; Mathematics","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":[0.0007058854,0.0005613455,0.0008721265,0.0007641103,0.0004344748,0.0008128057,0.002245463,0.0009444247,0.002105743],"category_scores_gemma":[0.001306308,0.0004628018,0.0007927967,0.0009075591,0.0002371206,0.001354151,0.0007423451,0.0008890637,0.0008484166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006419775,"about_ca_system_score_gemma":0.001470486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0199358,"about_ca_topic_score_gemma":0.02658171,"domain_scores_codex":[0.9997088,0.00004819241,0.00001824147,0.00009217304,0.00009515036,0.00003745123],"domain_scores_gemma":[0.9995265,0.0001604917,0.00003135578,0.00005851558,0.0001842758,0.00003883474],"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":[0.0001054893,0.0001107958,0.0010236,0.00003980265,0.00005193809,0.00005279052,0.00001842169,0.8883956,0.002315895,0.002405067,0.00301309,0.1024676],"study_design_scores_gemma":[0.0000022975,0.000002330343,0.00003113893,6.620771e-7,0.000002016336,0.000003614903,7.773681e-7,0.999337,0.0001687859,0.0003120428,0.000137548,0.000001698132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009599173,0.00009487433,0.986875,0.0001096336,0.00004631427,0.00005624985,0.0002984618,0.002334281,0.0005860292],"genre_scores_gemma":[0.3473021,0.0002102438,0.6455564,0.0002187696,0.000088086,0.0002943304,0.001829734,0.0004001725,0.004100184],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0199358,"threshold_uncertainty_score":0.03963953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02840940353173169,"score_gpt":0.2218033243992535,"score_spread":0.1933939208675218,"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."}}