{"id":"W2912669820","doi":"10.1007/s10092-019-0304-9","title":"Analysis of collocation methods for nonlinear Volterra integral equations of the third kind","year":2019,"lang":"en","type":"article","venue":"CALCOLO","topic":"Fractional Differential Equations Solutions","field":"Mathematics","cited_by":49,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"National Natural Science Foundation of China-Yunnan Joint Fund; National Natural Science Foundation of China","keywords":"Mathematics; Collocation (remote sensing); Piecewise; Volterra integral equation; Collocation method; Orthogonal collocation; Nonlinear system; Polynomial; Theory of computation; Integral equation; Applied mathematics; Convergence (economics); Operator (biology); Mathematical analysis; Numerical analysis; Differential equation; Computer science; Algorithm","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":[0.001822248,0.0006672423,0.0006449193,0.001149334,0.0006543223,0.001197726,0.0008557653,0.001538769,0.002523231],"category_scores_gemma":[0.007139135,0.0003396298,0.0006260552,0.0005863366,0.001417551,0.0009341158,0.001247172,0.001301125,0.000274298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009060286,"about_ca_system_score_gemma":0.0007595674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004099094,"about_ca_topic_score_gemma":0.002628638,"domain_scores_codex":[0.9995591,0.0002295877,0.00001742554,0.00003663157,0.000118665,0.00003854929],"domain_scores_gemma":[0.9946104,0.004057096,0.0002793098,0.0002211792,0.0007234411,0.0001085137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002633065,0.0001763387,0.002739527,0.0003722997,0.0001333318,0.0002348428,0.0007936065,0.6001027,0.01359984,0.3029462,0.002064654,0.07657342],"study_design_scores_gemma":[0.000004504672,0.00001468331,0.0001305208,0.00001665234,0.000005006735,0.00001390628,0.00002259194,0.9900748,0.0005626749,0.008454669,0.0006939304,0.000006038469],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05391636,0.0008030554,0.9401971,0.0002137267,0.0001226716,0.00003606747,0.00002558909,0.00008496685,0.004600476],"genre_scores_gemma":[0.8348072,0.0008289131,0.1533273,0.000116131,0.0001213429,0.0001393768,0.00009002525,0.0002425627,0.01032723],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004099094,"threshold_uncertainty_score":0.009637058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09647394307841585,"score_gpt":0.4338960731680414,"score_spread":0.3374221300896255,"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."}}