{"id":"W2609685805","doi":"10.1090/amsip/025/22","title":"Spline wavelets in numerical resolution of partial differential equations","year":2002,"lang":"en","type":"book-chapter","venue":"AMS/IP studies in advanced mathematics","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Collocation method; Mathematics; Orthogonal collocation; Spline (mechanical); Partial differential equation; Wavelet; Numerical partial differential equations; Mathematical analysis; Galerkin method; Legendre wavelet; Differential equation; Applied mathematics; Computer science; Finite element method; Physics; Wavelet transform; Ordinary differential equation; Discrete wavelet transform; Artificial intelligence","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":[0.0009979194,0.0007212122,0.000830257,0.001036881,0.0003025523,0.0009748622,0.0005403295,0.0009805312,0.002840914],"category_scores_gemma":[0.001252785,0.0004610533,0.0005919895,0.002103848,0.000728193,0.001129747,0.0007301115,0.001983197,0.002191894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003154901,"about_ca_system_score_gemma":0.0003268718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004597126,"about_ca_topic_score_gemma":0.0004214943,"domain_scores_codex":[0.9995017,0.0001492327,0.0000376058,0.00005446616,0.0002363972,0.0000205626],"domain_scores_gemma":[0.9996991,0.0001680147,0.00001561892,0.0000277393,0.00007490756,0.00001457977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005090908,0.00005596294,0.0002117485,0.001486512,0.00004745719,0.0003617101,0.0003955142,0.05154224,0.01058539,0.5935155,0.02476443,0.3169826],"study_design_scores_gemma":[0.00002350681,0.0001092587,0.0004199278,0.0004547147,0.00003859441,0.000737596,0.00007940438,0.1405644,0.00600797,0.4234681,0.4280348,0.00006183812],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004898712,0.1608588,0.7897493,0.001071355,0.002110934,0.00005026303,0.0001091457,0.0003465335,0.04080499],"genre_scores_gemma":[0.08401039,0.2855684,0.556207,0.0006541412,0.004336096,0.0002002106,0.0004609976,0.0004114163,0.06815139],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002840914,"threshold_uncertainty_score":0.009503782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1022737506254932,"score_gpt":0.3532178352102686,"score_spread":0.2509440845847755,"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."}}