{"id":"W4389579968","doi":"10.1029/2023ea003084","title":"Mineralogical Characterization From Geophysical Well Logs Using a Machine Learning Approach: Case Study for the Horn River Basin, Canada","year":2023,"lang":"en","type":"article","venue":"Earth and Space Science","topic":"Hydrocarbon exploration and reservoir analysis","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geological Survey of Canada","funders":"","keywords":"Geology; Geothermal gradient; Borehole; Oil shale; Structural basin; Reservoir modeling; Drilling; Well logging; Mineralogy; Petrology; Geochemistry; Geophysics; Geomorphology; Petroleum engineering; Geotechnical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002029785,0.00008529446,0.0001091101,0.00006109526,0.000413324,0.00009010833,0.00008822219,0.0000182017,0.000007374731],"category_scores_gemma":[0.00003991277,0.00005697456,0.0000231082,0.0006538276,0.00009683088,0.0001322815,0.00003602325,0.0001067135,0.000002909577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001454311,"about_ca_system_score_gemma":0.00005086565,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.08308955,"about_ca_topic_score_gemma":0.06719398,"domain_scores_codex":[0.9992853,0.00002814699,0.00008533862,0.0001954428,0.0002105828,0.0001951318],"domain_scores_gemma":[0.9996974,0.00005762393,0.00001870179,0.0001063641,0.00002906636,0.00009086557],"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":[0.00002349715,0.00008287367,0.03632065,0.00002703746,0.0001025165,0.0003478719,0.007909976,0.8497397,0.102089,0.00005630004,0.0001545068,0.003146103],"study_design_scores_gemma":[0.0001468545,0.00002010882,0.0130167,0.000001610036,0.00001882294,0.000008655716,0.001210985,0.98427,0.0001596195,0.000002722174,0.001062895,0.00008096511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9879194,0.00002239527,0.01151279,0.0001773168,0.00009199745,0.0001686868,0.00001216051,0.00005761196,0.00003761122],"genre_scores_gemma":[0.9993401,0.00001335794,0.0002411689,0.00003556024,0.00007174588,0.00000853929,0.00002041717,0.000006808792,0.000262282],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1345304,"threshold_uncertainty_score":0.9498273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02105345659396306,"score_gpt":0.2202356114752905,"score_spread":0.1991821548813274,"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."}}