{"id":"W2756219328","doi":"10.1016/j.cam.2017.09.014","title":"Time discretization and stability regions for dissipative–dispersive Kuramoto–Sivashinsky equation arising in turbulent gas flow over laminar liquid","year":2017,"lang":"en","type":"article","venue":"Journal of Computational and Applied Mathematics","topic":"Nonlinear Dynamics and Pattern Formation","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Mathematics; Dissipative system; Discretization; Laminar flow; Turbulence; Flow (mathematics); Partial differential equation; Crank–Nicolson method; Temporal discretization; Mathematical analysis; Stability (learning theory); Ordinary differential equation; Applied mathematics; Differential equation; Mechanics; Physics; Thermodynamics; Geometry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000452243,0.0004341408,0.0002908793,0.0006016802,0.0006561105,0.001039321,0.000656068,0.0007412353,0.001904024],"category_scores_gemma":[0.001735476,0.0002432002,0.0004239799,0.0002233921,0.001275774,0.0008420708,0.0007269585,0.0009615702,0.000136005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006305225,"about_ca_system_score_gemma":0.0005885294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003685373,"about_ca_topic_score_gemma":0.001787239,"domain_scores_codex":[0.9999099,0.00003819112,0.00000616528,0.00001343427,0.00001720649,0.00001506672],"domain_scores_gemma":[0.9992988,0.0003673514,0.000121396,0.00003283166,0.0001182383,0.00006133516],"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":[0.0007432177,0.0002422226,0.005963371,0.000276301,0.00008794334,0.0006050448,0.001233184,0.5013136,0.06687911,0.4038194,0.002128473,0.01670808],"study_design_scores_gemma":[0.00001427396,0.00002474115,0.0004419204,0.00001216056,0.000006880828,0.00002253784,0.00006452265,0.9865498,0.001548307,0.01097608,0.0003267117,0.00001216672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7970821,0.001915222,0.176512,0.001141151,0.0002277832,0.00007404015,0.0001220757,0.0001444836,0.02278127],"genre_scores_gemma":[0.9868902,0.0002559368,0.008125772,0.0000536174,0.00003932592,0.000032215,0.00006320597,0.00003742506,0.004502278],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003685373,"threshold_uncertainty_score":0.007327855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02423579430049282,"score_gpt":0.266677110034804,"score_spread":0.2424413157343112,"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."}}