{"id":"W4414345351","doi":"10.1016/j.cpc.2025.109866","title":"JAX-WSPM: A GPU-accelerated parallel framework based on the JAX library for modeling water flow and solute transport in unsaturated porous media using an implicit finite element method","year":2025,"lang":"en","type":"article","venue":"Computer Physics Communications","topic":"Soil and Unsaturated Flow","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa; École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Fondation OCP; Ministère de l'Enseignement Supérieur, de la Recherche Scientifique et de la Formation des Cadres; Centre National pour la Recherche Scientifique et Technique","keywords":"Python (programming language); Finite element method; Solver; Scalability; Porous medium; Richards equation; Partial differential equation; Speedup; Robustness (evolution)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001968198,0.0002709066,0.000290864,0.000121447,0.0003007112,0.0001277691,0.0006612577,0.0001459412,0.000004885279],"category_scores_gemma":[0.000005898738,0.0002099759,0.00007317535,0.0005017246,0.00004367375,0.0002759942,0.0001166299,0.0005691262,0.000001250133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004222144,"about_ca_system_score_gemma":0.00005475593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003266971,"about_ca_topic_score_gemma":0.00002226888,"domain_scores_codex":[0.9987534,0.0001557404,0.0003947052,0.0002630063,0.00009535295,0.0003377674],"domain_scores_gemma":[0.9979602,0.000687603,0.00003320635,0.00118443,0.00007201637,0.00006259339],"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":[0.00002749913,0.0001117376,0.00005720348,0.00003096736,0.00006991161,7.683321e-7,0.001312047,0.9849071,0.0004184471,0.003094054,0.00006373831,0.009906555],"study_design_scores_gemma":[0.000515405,0.00002281458,0.0001675455,0.0001867643,0.00004542871,4.558555e-7,0.00005673131,0.9884194,0.0003965024,0.009740969,0.0002017582,0.0002462064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03057283,0.0001711771,0.967011,0.00110454,0.0001551465,0.0005197657,0.00006979425,0.0002827524,0.0001129556],"genre_scores_gemma":[0.5699218,0.00007730821,0.428389,0.0006955107,0.00006506153,0.0001214622,0.0006816357,0.00004523802,0.000003019808],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.539349,"threshold_uncertainty_score":0.8562568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05624015830020769,"score_gpt":0.2831482989779233,"score_spread":0.2269081406777156,"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."}}