{"id":"W1970270154","doi":"10.1088/1742-2132/9/5/534","title":"Investigation of the geothermal state of sedimentary basins using oil industry thermal data: case study from Northern Alberta exhibiting the need to systematically remove biased data","year":2012,"lang":"en","type":"article","venue":"Journal of Geophysics and Engineering","topic":"Hydrocarbon exploration and reservoir analysis","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Geothermal gradient; Petroleum engineering; Structural basin; Geology; Sedimentary basin; Drill; Environmental science; Thermal; Sedimentary rock; Data quality; Geothermal exploration; Geothermal energy; Earth science; Geochemistry; Meteorology; Geomorphology; Engineering; Geophysics; Geography; Mechanical 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":[0.0005907342,0.0001401295,0.0002995394,0.00007214707,0.00005563733,0.00003383848,0.0004035604,0.00004300968,0.000002689123],"category_scores_gemma":[0.00006533408,0.00008781213,0.00004842292,0.00029351,0.0000223131,0.0005223546,0.0002260994,0.0003140803,3.129309e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002827116,"about_ca_system_score_gemma":0.00003331361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001605182,"about_ca_topic_score_gemma":0.000255908,"domain_scores_codex":[0.9987962,0.00006890179,0.0005886899,0.0000924863,0.0002959815,0.0001578029],"domain_scores_gemma":[0.9988298,0.0001722788,0.0002376397,0.0005816303,0.00006995437,0.0001086614],"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":[0.00001092963,0.00005310887,0.01735828,0.0002064816,0.0007322592,0.00002410806,0.00824679,0.8893747,0.08282816,0.000002115955,0.00001093947,0.001152169],"study_design_scores_gemma":[0.0003984094,0.00002949315,0.00698092,0.0004084662,0.0004010415,0.00006917628,0.005150592,0.9833681,0.003018434,0.000004023782,0.00001433475,0.0001569841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978812,0.0001490757,0.001542453,0.00007785693,0.0001790459,0.00008804077,0.00006727141,0.000006483291,0.000008594426],"genre_scores_gemma":[0.9991881,0.000005108483,0.0004968412,0.0000232083,0.0002473718,8.278182e-7,0.000008932871,0.00002648735,0.000003110617],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09399346,"threshold_uncertainty_score":0.3580875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04370389631377052,"score_gpt":0.2384740237568179,"score_spread":0.1947701274430474,"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."}}