{"id":"W4280646467","doi":"10.3390/en15103675","title":"Well-Logging-Based Lithology Classification Using Machine Learning Methods for High-Quality Reservoir Identification: A Case Study of Baikouquan Formation in Mahu Area of Junggar Basin, NW China","year":2022,"lang":"en","type":"article","venue":"Energies","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Bank of Canada","funders":"National Natural Science Foundation of China","keywords":"Lithology; Identification (biology); Logging; Support vector machine; Random forest; Geology; Ensemble learning; Structural basin; Receiver operating characteristic; Conglomerate; Computer science; Artificial intelligence; Machine learning; Data mining; Pattern recognition (psychology); Petrology; Geomorphology","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":[0.0009514286,0.0005890437,0.0005404173,0.001466383,0.0006038653,0.0007479809,0.0008857425,0.0007734004,0.0002838877],"category_scores_gemma":[0.0009708399,0.0001927278,0.0004843753,0.001640832,0.0005339195,0.0007107506,0.0005356799,0.0002772908,0.0001003546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001389574,"about_ca_system_score_gemma":0.001224632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07697055,"about_ca_topic_score_gemma":0.1216159,"domain_scores_codex":[0.9995864,0.00009329501,0.00003962951,0.00008152085,0.0001148967,0.0000842629],"domain_scores_gemma":[0.9994034,0.0002036994,0.00008366571,0.00005791067,0.0001918088,0.00005956604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003122827,0.0005081966,0.7003807,0.0003032497,0.0002437706,0.00812128,0.00138477,0.1694008,0.01000724,0.0008428285,0.002035032,0.1064598],"study_design_scores_gemma":[0.00003212884,0.0001647998,0.3050141,0.00006720779,0.0001292909,0.0004686948,0.003237018,0.6804908,0.007847794,0.0007070401,0.001775092,0.00006610712],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967895,0.0001736451,0.002120127,0.0001224083,0.000005620637,0.00002779459,0.0002169414,0.00003850956,0.0005054805],"genre_scores_gemma":[0.9961002,0.000104708,0.003013046,0.00001447907,0.000006737774,0.00001596006,0.0003593223,0.00000470046,0.0003807883],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07697055,"threshold_uncertainty_score":0.1530451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07918405858139858,"score_gpt":0.3605456546544115,"score_spread":0.2813615960730129,"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."}}