{"id":"W4415515968","doi":"10.1016/j.pce.2025.104159","title":"Machine learning-driven porosity prediction: A case study from the Cretaceous Mata Series, Canterbury Basin, New Zealand","year":2025,"lang":"en","type":"article","venue":"Physics and Chemistry of the Earth Parts A/B/C","topic":"Hydrocarbon exploration and reservoir analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Government of Northwest Territories","funders":"China University of Petroleum, Beijing","keywords":"Porosity; Artificial neural network; Linear regression; Mean squared error; Series (stratigraphy); Submarine pipeline; Correlation coefficient; Effective porosity","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006658505,0.0006275797,0.000533272,0.0007438145,0.0009121108,0.001185626,0.001265128,0.0008227132,0.00123457],"category_scores_gemma":[0.003774063,0.0003420014,0.0004429682,0.001991295,0.0009804982,0.0006976192,0.0005244354,0.0006208651,0.0001649116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004996862,"about_ca_system_score_gemma":0.00403199,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.7592405,"about_ca_topic_score_gemma":0.7617326,"domain_scores_codex":[0.9997392,0.00003184752,0.00002012138,0.00005712052,0.00009711672,0.00005450353],"domain_scores_gemma":[0.9985591,0.0008238425,0.0001382182,0.00009336111,0.0003014587,0.00008416041],"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":[0.0004773853,0.0003219855,0.1104501,0.0002861503,0.0001360056,0.003349133,0.0008902657,0.8042115,0.006828994,0.001410058,0.004108846,0.06752959],"study_design_scores_gemma":[0.0001217615,0.0001392746,0.08888976,0.00004027866,0.00006675874,0.0003328166,0.000877402,0.900206,0.004964641,0.001005592,0.003297412,0.00005831153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.992312,0.0001892151,0.003391726,0.0002923445,0.000009098945,0.00005779741,0.0008891243,0.0002253589,0.002633211],"genre_scores_gemma":[0.9943837,0.0001324319,0.00353805,0.00001338418,0.000003738661,0.00001537841,0.0006471243,0.00003700099,0.001229098],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7592405,"threshold_uncertainty_score":0.4843547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008203776193655265,"score_gpt":0.1999341790197307,"score_spread":0.1917304028260754,"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."}}