{"id":"W2586145489","doi":"10.2118/184964-ms","title":"Pore Network and Morphological Characterization of Pore-Level Structures","year":2017,"lang":"en","type":"article","venue":"SPE Canada Heavy Oil Technical Conference","topic":"Hydrocarbon exploration and reservoir analysis","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Characterization (materials science); Petrophysics; Oil shale; Reservoir modeling; Permeability (electromagnetism); Characterisation of pore space in soil; Voxel; Relative permeability; Scaling; Computer science; Artificial neural network; Materials science; Geology; Biological system; Porosity; Mineralogy; Artificial intelligence; Geometry; Petroleum engineering; Nanotechnology; Geotechnical engineering; Mathematics; Chemistry","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":[0.00008189109,0.0001322595,0.0002625122,0.00002757738,0.0001318739,0.00005549862,0.0002843953,0.0001223881,0.0001030808],"category_scores_gemma":[0.00007915226,0.0001120922,0.00003181864,0.00007100066,0.0001394845,0.00008832256,0.00007017222,0.0001940179,4.5907e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003911715,"about_ca_system_score_gemma":0.0001351389,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01146554,"about_ca_topic_score_gemma":0.132016,"domain_scores_codex":[0.9991512,0.00001760731,0.0002495887,0.0001720325,0.0002117741,0.0001978431],"domain_scores_gemma":[0.9993167,0.00001753888,0.00008593091,0.0003760849,0.00006954957,0.000134228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002561586,0.0001160694,0.01935432,0.0009940559,0.0005692855,0.001152654,0.0002295658,0.0268634,0.6560764,0.08335908,0.01838953,0.1926395],"study_design_scores_gemma":[0.001816784,0.0002603199,0.6603383,0.0004421062,0.00025097,0.000217007,0.0001369434,0.2237211,0.05445153,0.01390764,0.042034,0.002423383],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9711722,0.0001511886,0.01386016,0.002864534,0.0003861216,0.0001268517,0.0001619551,0.000210686,0.01106628],"genre_scores_gemma":[0.9986942,0.000255301,0.0006539754,0.00008835923,0.00006602746,0.000006100312,0.00008167559,0.00001130447,0.0001430327],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6409839,"threshold_uncertainty_score":0.9951172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03103747644963819,"score_gpt":0.2336443575504895,"score_spread":0.2026068811008513,"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."}}