{"id":"W3046995003","doi":"10.1021/acs.energyfuels.0c01801","title":"Quantitative Statistical Evaluation of Micro Residual Oil after Polymer Flooding Based on X-ray Micro Computed-Tomography Scanning","year":2020,"lang":"en","type":"article","venue":"Energy & Fuels","topic":"Enhanced Oil Recovery Techniques","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Residual oil; Polymer; Residual; Materials science; Permeability (electromagnetism); Viscous fingering; Enhanced oil recovery; Petroleum engineering; Composite material; Porosity; Chemical engineering; Porous medium; Chemistry; Geology; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003025878,0.0002482503,0.0003063683,0.0002395636,0.00003855372,0.00003242241,0.0001579939,0.0001150763,0.0001540809],"category_scores_gemma":[0.00009078026,0.0002689183,0.00009714003,0.0004123125,0.00007081088,0.0001255668,0.00003037485,0.0001862913,0.000008903795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006975263,"about_ca_system_score_gemma":0.00005358441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003218248,"about_ca_topic_score_gemma":0.00001369469,"domain_scores_codex":[0.9983373,0.0001805907,0.0003762493,0.000325368,0.000496546,0.0002839802],"domain_scores_gemma":[0.9991551,0.0003050454,0.00007532597,0.0002086677,0.0001461631,0.0001096782],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000211539,0.00005370599,0.0003528376,0.0001560079,0.0001410178,0.00001992812,0.0005162053,0.06231644,0.9184614,0.0009582635,0.001691941,0.01512066],"study_design_scores_gemma":[0.0004521581,0.0002315602,0.001133217,0.0002285821,0.0000749297,6.069489e-7,0.00004459581,0.06144986,0.9357354,0.0001493468,0.0002157654,0.0002839423],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8110491,0.002212192,0.1819251,0.0001152224,0.0002479355,0.00008370167,0.0002019283,0.0005094723,0.00365538],"genre_scores_gemma":[0.9403386,0.00002167087,0.05900138,0.0003426038,0.00008730128,0.00005881085,0.00006992429,0.00006317748,0.00001656233],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1292895,"threshold_uncertainty_score":0.9999763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01971928151575498,"score_gpt":0.2609791671577462,"score_spread":0.2412598856419912,"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."}}