{"id":"W2594458510","doi":"10.1190/tle36030227.1","title":"The use of predictive analytics for hydrocarbon exploration in the Denver-Julesburg Basin","year":2017,"lang":"en","type":"article","venue":"The Leading Edge","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Alberta","keywords":"Variable (mathematics); Set (abstract data type); Isopach map; Nonparametric regression; Nonparametric statistics; Linear regression; Random variable; Computer science; Regression; Econometrics; Data mining; Statistics; Mathematics; Machine learning; Geology; Structural basin","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0009801607,0.00007005336,0.00008895582,0.00001561346,0.0005318423,0.0002281796,0.001373118,0.00003830216,3.395898e-7],"category_scores_gemma":[0.00085283,0.00003467335,0.00004696102,0.00008265336,0.000141716,0.0003187815,0.0001506874,0.0001175828,0.000002941406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001552752,"about_ca_system_score_gemma":0.00002338777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002791747,"about_ca_topic_score_gemma":0.0000325828,"domain_scores_codex":[0.9993484,0.00008285145,0.0001409581,0.0001391047,0.0001255757,0.0001631286],"domain_scores_gemma":[0.9980831,0.00078109,0.0001762657,0.0008882146,0.00005874798,0.00001256896],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004599011,0.0005128155,0.04323538,0.0003247175,0.0003913219,0.000040715,0.1136658,0.06638615,0.01447768,0.5043684,0.1139704,0.1421668],"study_design_scores_gemma":[0.0003889965,0.0001085178,0.01048617,0.00008741698,0.00003053925,0.000007508213,0.0005356416,0.8197427,0.01127033,0.07029755,0.08688109,0.0001635625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2043048,0.0001676016,0.6811631,0.09489238,0.001101257,0.001375093,0.000008646842,0.00009745922,0.01688971],"genre_scores_gemma":[0.998069,0.00001658785,0.0004816884,0.0001468083,0.00008708156,0.00003001497,9.957943e-7,0.000001607714,0.001166245],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7937642,"threshold_uncertainty_score":0.4090555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1200735047592907,"score_gpt":0.2889891579352136,"score_spread":0.1689156531759229,"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."}}