{"id":"W4403561367","doi":"10.1016/j.coal.2024.104625","title":"An improved convolutional architecture for quantitative characterization of pore networks in fine-grained rocks using FIB-SEM","year":2024,"lang":"en","type":"article","venue":"International Journal of Coal Geology","topic":"Hydrocarbon exploration and reservoir analysis","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"Petroleum Technology Research Centre; University of Regina","funders":"","keywords":"Characterization (materials science); Geology; Porosity; Mineralogy; Materials science; Nanotechnology; Geotechnical engineering","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":[0.0004149541,0.0006597863,0.0003200175,0.0007232581,0.0002724719,0.000627148,0.001108182,0.0007376978,0.002197873],"category_scores_gemma":[0.0007656588,0.0003246515,0.0004556253,0.0004542324,0.0002936009,0.0007378922,0.0006126871,0.0006551566,0.000804225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005491828,"about_ca_system_score_gemma":0.0008509065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008172465,"about_ca_topic_score_gemma":0.01625221,"domain_scores_codex":[0.9998806,0.00000946976,0.000005053897,0.00003335175,0.00004696573,0.00002460989],"domain_scores_gemma":[0.999725,0.00006343691,0.00002587214,0.00004864173,0.0001204863,0.00001655147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000307383,0.0002209848,0.008227796,0.0003003278,0.0002249272,0.000199821,0.00013736,0.3129374,0.222961,0.007014331,0.009874238,0.4375944],"study_design_scores_gemma":[0.00000385659,0.00002609325,0.001909147,0.00000621182,0.00001580079,0.00003972451,0.00001059193,0.9734908,0.02226794,0.001240814,0.000980364,0.000008757589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1523425,0.0004584914,0.8319809,0.0002882872,0.00008773717,0.00007524682,0.00166067,0.009802255,0.003303912],"genre_scores_gemma":[0.6768183,0.0003242645,0.3138777,0.0001703372,0.00003068911,0.0001179984,0.003141417,0.0003647382,0.005154577],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008172465,"threshold_uncertainty_score":0.01624978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01338387616781199,"score_gpt":0.2835862682946981,"score_spread":0.2702023921268861,"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."}}