{"id":"W2008407303","doi":"10.1016/j.cam.2004.12.036","title":"Method of lines solutions of the extended Boussinesq equations","year":2005,"lang":"en","type":"article","venue":"Journal of Computational and Applied Mathematics","topic":"Coastal and Marine Dynamics","field":"Earth and Planetary Sciences","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Université Laval; W.F. Baird & Associates Coastal Engineers (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada; University of Illinois at Urbana-Champaign; Université du Québec à Rimouski; University of Chicago","keywords":"Mathematics; Boussinesq approximation (buoyancy); Truncation (statistics); Truncation error; Numerical analysis; Mathematical analysis; Range (aeronautics); Scheme (mathematics); Applied mathematics; Physics; Mechanics","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":[0.000274442,0.00005388956,0.0001567192,0.00005106733,0.00006537739,0.000009022391,0.00009736096,0.00001949538,0.00005391688],"category_scores_gemma":[0.00003594757,0.00003181802,0.00006056212,0.0001228598,0.00006703748,0.00005350562,0.00002069603,0.0000661257,0.000001065697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001632315,"about_ca_system_score_gemma":0.0000655762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007765189,"about_ca_topic_score_gemma":0.00004762518,"domain_scores_codex":[0.9992664,0.00001159298,0.0004022739,0.00003646639,0.0002221211,0.00006113807],"domain_scores_gemma":[0.9989658,0.000410313,0.000410551,0.00004400633,0.0001382968,0.00003103613],"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":[0.00002445072,0.0001896012,0.0003939411,0.0001243444,0.00006099122,3.027857e-7,0.0006832404,0.6268902,0.0003776278,0.1626107,0.0001416567,0.208503],"study_design_scores_gemma":[0.0003857451,0.00005896178,0.02964491,0.00004882218,0.00007385155,0.00009352884,0.0002869587,0.6070712,0.000173891,0.3616768,0.0004098107,0.00007552181],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1543205,0.0001478422,0.8399348,0.001304933,0.00008031555,0.0001167389,0.00005637738,0.000004686675,0.004033798],"genre_scores_gemma":[0.6905862,0.00000778995,0.3092676,0.00002893681,0.00005015514,1.208656e-7,0.000003826268,0.000001164237,0.00005421585],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5362657,"threshold_uncertainty_score":0.1297501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02064152533288149,"score_gpt":0.2517597235674231,"score_spread":0.2311181982345416,"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."}}