{"id":"W2496443041","doi":"10.1615/jpormedia.v19.i5.30","title":"IMPROVEMENT OF PERMEABILITY MODELS USING LARGE MERCURY INJECTION CAPILLARY PRESSURE DATASET FOR MIDDLE EAST CARBONATE RESERVOIRS","year":2016,"lang":"en","type":"article","venue":"Journal of Porous Media","topic":"Petroleum Processing and Analysis","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Petroleum Research Newfoundland and Labrador; Memorial University of Newfoundland","funders":"","keywords":"Linearization; Nonlinear regression; Permeability (electromagnetism); Nonlinear system; Linear regression; Regression; Relative permeability; Regression analysis; Mathematics; Geology; Statistics; Chemistry; Porosity; Geotechnical engineering; Physics","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":[0.001720478,0.001193791,0.0006693332,0.001052101,0.0004124213,0.0009808405,0.001028535,0.0008600213,0.0005793463],"category_scores_gemma":[0.004661055,0.0004327649,0.00115999,0.000892122,0.0002646874,0.001883265,0.0005905458,0.0008104854,0.0002956046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009254894,"about_ca_system_score_gemma":0.001542013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03553632,"about_ca_topic_score_gemma":0.02836559,"domain_scores_codex":[0.9995245,0.0001430864,0.00004631378,0.0001531648,0.00007853605,0.00005439634],"domain_scores_gemma":[0.9988704,0.0004880541,0.0001317503,0.0001755504,0.0002882394,0.00004598702],"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":[0.0001777558,0.0002439316,0.02985748,0.0001110911,0.0002440456,0.0001746955,0.00009048091,0.8963256,0.008196102,0.0006612166,0.001759514,0.06215798],"study_design_scores_gemma":[0.0000113888,0.00002258108,0.005593049,0.000006233621,0.00002080721,0.00001536336,0.00002659121,0.9909712,0.0027819,0.0001815861,0.0003497578,0.00001952548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8670039,0.0004221067,0.1232517,0.0004658782,0.00007627166,0.00009631109,0.00281905,0.004185094,0.001679723],"genre_scores_gemma":[0.9542237,0.0001524502,0.04107944,0.00004027518,0.00001372161,0.00004207359,0.003821813,0.0001565617,0.0004700329],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03553632,"threshold_uncertainty_score":0.07065892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03947924711328774,"score_gpt":0.2664446303966665,"score_spread":0.2269653832833788,"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."}}