{"id":"W3003770415","doi":"10.1155/2020/5387183","title":"Generation of Synthetic Density Log Data Using Deep Learning Algorithm at the Golden Field in Alberta, Canada","year":2020,"lang":"en","type":"article","venue":"Geofluids","topic":"Hydrocarbon exploration and reservoir analysis","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Korea Institute of Energy Technology Evaluation and Planning; National Research Foundation of Korea; Ministry of Trade, Industry and Energy; Korea Institute of Geoscience and Mineral Resources; National Research Foundation","keywords":"Field (mathematics); Artificial neural network; Well logging; Geology; Algorithm; Deep learning; Computer science; Point (geometry); Data mining; Preprocessor; Artificial intelligence; Geophysics; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002761456,0.0007596231,0.000306143,0.001075163,0.0005246799,0.0005066431,0.001235548,0.0005508675,0.001674366],"category_scores_gemma":[0.0009197372,0.0003045616,0.0004127535,0.001238482,0.0005782896,0.0004170523,0.0004938857,0.0005178541,0.0004034906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002880048,"about_ca_system_score_gemma":0.002981437,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4558803,"about_ca_topic_score_gemma":0.5269918,"domain_scores_codex":[0.99978,0.00001532054,0.00000803861,0.00003985939,0.0001102873,0.00004662733],"domain_scores_gemma":[0.9996295,0.00005188532,0.00001878395,0.00003173265,0.0002222331,0.0000457819],"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":[0.0003253924,0.0003400772,0.04790271,0.0001631504,0.00004732551,0.001265708,0.0001869327,0.8608321,0.006578673,0.001574355,0.01211084,0.06867275],"study_design_scores_gemma":[0.00002887562,0.00002559012,0.01377961,0.00001180162,0.000008145932,0.00003915006,0.0001712152,0.980807,0.002876517,0.0005745567,0.001651468,0.00002613768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9231554,0.0002324185,0.04935502,0.0005269547,0.0001467945,0.0002140974,0.01457112,0.004101948,0.007696273],"genre_scores_gemma":[0.9585594,0.0001235775,0.025874,0.00004653938,0.00001105346,0.0000730053,0.01241743,0.0001175176,0.002777485],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5441197,"threshold_uncertainty_score":0.9064535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03566398208406647,"score_gpt":0.2298924848443752,"score_spread":0.1942285027603087,"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."}}