{"id":"W4402043317","doi":"10.1016/j.advwatres.2024.104799","title":"A computationally efficient queue-based algorithm for simulating volume-controlled drainage under the influence of gravity on volumetric images of porous materials","year":2024,"lang":"en","type":"article","venue":"Advances in Water Resources","topic":"Enhanced Oil Recovery Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Canarie","keywords":"Queue; Volume (thermodynamics); Drainage; Algorithm; Porosity; Porous medium; Computer science; Geology; Petroleum engineering; Geotechnical engineering; Physics; Thermodynamics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004194195,0.000574475,0.000576181,0.0003709962,0.0005323866,0.000903923,0.00206376,0.001246776,0.003168681],"category_scores_gemma":[0.0008879238,0.000381686,0.0005738854,0.0005349906,0.0003942333,0.0007524585,0.0007612522,0.0008846106,0.0005672732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009635891,"about_ca_system_score_gemma":0.001610061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01089219,"about_ca_topic_score_gemma":0.009504535,"domain_scores_codex":[0.999873,0.00002249578,0.000008538866,0.00002127569,0.00004980346,0.00002476389],"domain_scores_gemma":[0.9996727,0.0001398577,0.00002621838,0.0000193717,0.000103035,0.00003879733],"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":[0.0001899662,0.0001350083,0.001034338,0.0001427785,0.00004452872,0.0001424337,0.0001041545,0.9055005,0.02216318,0.01001339,0.002205517,0.05832425],"study_design_scores_gemma":[0.00001341134,0.0000114194,0.00002885983,0.000001176585,0.00000174971,0.000005404067,0.000002634438,0.9984451,0.0008256742,0.0003720411,0.0002894924,0.000003078549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0277806,0.0001169819,0.9684645,0.0001253585,0.00006974576,0.00009642141,0.0001465708,0.001495719,0.001704005],"genre_scores_gemma":[0.3562062,0.0001998996,0.638651,0.0001295955,0.00003018727,0.0003356767,0.000483783,0.0003910489,0.003572766],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01089219,"threshold_uncertainty_score":0.02165759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004252481136250607,"score_gpt":0.2466751497135062,"score_spread":0.2424226685772556,"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."}}