{"id":"W4377227124","doi":"10.1016/j.geoen.2023.211923","title":"A quantitative evaluation model for biodegraded reservoirs based on multinomial logistic regression","year":2023,"lang":"en","type":"article","venue":"Geoenergy Science and Engineering","topic":"Hydrocarbon exploration and reservoir analysis","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"CNPC Chuanqing Drilling Engineering Company Limited; China Scholarship Council","keywords":"Multinomial logistic regression; Identification (biology); Logistic regression; Petroleum engineering; Multinomial distribution; Methane; Statistics; Well logging; Software; Soil science; Data mining; Computer science; Environmental science; Mathematics; Geology; Chemistry; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009426848,0.0001394606,0.0001360531,0.0006166147,0.0001842385,0.00007038573,0.0001505522,0.00005356155,0.000002668617],"category_scores_gemma":[0.0005621305,0.0001175733,0.00004314907,0.001193548,0.00006886525,0.0002159042,0.00002293162,0.0000754471,0.000006930256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009328475,"about_ca_system_score_gemma":0.00006759494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009278577,"about_ca_topic_score_gemma":0.00001322417,"domain_scores_codex":[0.9987592,0.00001022453,0.0001623745,0.0002634564,0.0004794998,0.000325212],"domain_scores_gemma":[0.9994453,0.00009989396,0.00002011136,0.0001789832,0.000142884,0.0001128342],"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":[0.00001066849,0.000004233463,0.000005651689,0.00002374541,0.000005699048,6.83703e-7,0.000161716,0.9806145,0.01776303,0.0003980266,0.0001748774,0.0008371308],"study_design_scores_gemma":[0.000341485,0.00003846183,0.0001471007,0.00004012341,0.00001405504,2.063101e-7,0.00009122946,0.9965073,0.002455679,0.00009924467,0.0001117888,0.0001533017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6916585,0.0001077917,0.3057007,0.0005184116,0.0004694664,0.0003546782,0.000018522,0.0007750407,0.0003969061],"genre_scores_gemma":[0.9964852,0.00003330318,0.003201291,0.00002790295,0.0000316755,0.0001239398,0.00003042276,0.00001853137,0.00004777686],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3048266,"threshold_uncertainty_score":0.47945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08439374124361597,"score_gpt":0.3093868258372234,"score_spread":0.2249930845936074,"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."}}