{"id":"W2920403792","doi":"10.1002/nag.2913","title":"A probabilistic approach for computing water retention of particulate systems from statistics of grain size and tessellated pore network","year":2019,"lang":"en","type":"article","venue":"International Journal for Numerical and Analytical Methods in Geomechanics","topic":"Soil and Unsaturated Flow","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Energi Simulation","keywords":"Water retention curve; Particle-size distribution; Soil water; Porous medium; Void ratio; Capillary action; Particle size; Granular material; Wetting; SPHERES; Saturation (graph theory); Geotechnical engineering; Mathematics; Porosity; Mechanics; Water retention; Materials science; Engineering; Soil science; Geology; Physics","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":[0.0008692113,0.0003124946,0.0004228242,0.00149842,0.0003309301,0.0009201948,0.00093815,0.000595757,0.001022494],"category_scores_gemma":[0.004628778,0.0004044013,0.0007182545,0.001006465,0.0006500187,0.001159873,0.0008334173,0.0004951695,0.0002123015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009938381,"about_ca_system_score_gemma":0.0007940006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006360026,"about_ca_topic_score_gemma":0.005035925,"domain_scores_codex":[0.9996849,0.0000625958,0.00002498633,0.00006681295,0.0001168769,0.00004374602],"domain_scores_gemma":[0.9983459,0.001001235,0.0001981587,0.0001570286,0.000234253,0.00006346379],"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":[0.00002872836,0.00001120182,0.001509174,0.0000229202,0.00002121501,0.0000283619,0.00002553283,0.9637094,0.002656281,0.01806715,0.0001468419,0.01377324],"study_design_scores_gemma":[0.000001022621,0.000004578733,0.0002206161,0.000001073095,0.000001332112,0.000007351087,0.00000281582,0.9949974,0.000361403,0.004284287,0.0001152169,0.000002925011],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03492109,0.00005015126,0.9639458,0.00003262291,0.000004675804,0.00001437932,0.0001152948,0.0004648605,0.0004511952],"genre_scores_gemma":[0.7510065,0.0001389604,0.2472832,0.00003734061,0.00002438737,0.0001244428,0.0003653166,0.0001375739,0.0008822773],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006360026,"threshold_uncertainty_score":0.01264602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02140790353712534,"score_gpt":0.3047363799274381,"score_spread":0.2833284763903127,"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."}}