{"id":"W1865313205","doi":"10.3968/5122","title":"A Big Data Mining in Petroleum Exploration and Development","year":2014,"lang":"en","type":"article","venue":"Advances in petroleum exploration and development","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Support vector machine; Nonlinear system; Artificial neural network; Artificial intelligence; Algorithm; Dimension (graph theory); Data mining; Machine learning; Residual; Dimensionality reduction; Computer science; Pattern recognition (psychology); Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000962913,0.0002248659,0.0002461143,0.0002360418,0.0001784934,0.0001515887,0.0004441986,0.0000733327,0.000004008755],"category_scores_gemma":[0.0001630772,0.0002213699,0.00000706434,0.0002417144,0.00004668136,0.002526626,0.0005744369,0.0001411075,0.00001175213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006797461,"about_ca_system_score_gemma":0.0001536733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003978837,"about_ca_topic_score_gemma":0.0004355779,"domain_scores_codex":[0.9979916,0.00009111666,0.0005578907,0.0007432299,0.0002805183,0.0003355954],"domain_scores_gemma":[0.9991661,0.0001078148,0.0001595282,0.0004201314,0.00005029445,0.00009615645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002936958,0.0001041083,0.01447275,0.0001161149,0.000009109574,0.00001440591,0.009737001,0.01226798,0.0001400037,0.001862413,0.00009311311,0.9611536],"study_design_scores_gemma":[0.001879886,0.00007742866,0.006291199,0.0002516738,0.000002279037,0.0000184721,0.003337734,0.2631643,0.002844418,0.004041825,0.7173499,0.0007409443],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01213789,0.0009556246,0.981233,0.002632196,0.0003736946,0.000133709,7.963446e-7,0.00008715805,0.002445934],"genre_scores_gemma":[0.8630606,0.0008551783,0.1352275,0.0002804203,0.00006351272,0.0001377163,0.0001053539,0.000005834583,0.0002638134],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9604127,"threshold_uncertainty_score":0.9027201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06086405424033452,"score_gpt":0.2645926989825979,"score_spread":0.2037286447422633,"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."}}