{"id":"W1823963912","doi":"10.1007/978-1-4684-9244-6_8","title":"Using the Picard Contraction Mapping to Solve Inverse Problems in Ordinary Differential Equations","year":2002,"lang":"en","type":"book-chapter","venue":"The IMA volumes in mathematics and its applications","topic":"Matrix Theory and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; University of Guelph","funders":"","keywords":"Contraction (grammar); Contraction mapping; Inverse; Mathematics; Ordinary differential equation; Applied mathematics; Mathematical analysis; Computer science; Calculus (dental); Differential equation; Medicine; Fixed point; Geometry; Internal medicine; Orthodontics","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":[0.0006514175,0.001005433,0.001191378,0.000707406,0.0006795002,0.001308109,0.0008330859,0.0009354977,0.002318695],"category_scores_gemma":[0.002074762,0.0005009593,0.0009876173,0.0006078916,0.002117991,0.001816418,0.001894307,0.002508894,0.0005182481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003829884,"about_ca_system_score_gemma":0.0006568537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009537425,"about_ca_topic_score_gemma":0.001118966,"domain_scores_codex":[0.9997963,0.00007483694,0.000009949409,0.00003028213,0.00007265338,0.00001601993],"domain_scores_gemma":[0.9997532,0.0001415647,0.00001594001,0.00003012215,0.00003871385,0.00002040007],"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":[0.00002513743,0.00002955883,0.00009425094,0.0001373149,0.0000219942,0.0001066278,0.0002663072,0.01735093,0.003731249,0.9377264,0.002435787,0.03807452],"study_design_scores_gemma":[0.00002008212,0.00005645049,0.0001435657,0.0000327768,0.00001906805,0.0002173669,0.0000742112,0.2189254,0.002202521,0.7611133,0.0171685,0.00002679127],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02516277,0.002655476,0.928166,0.0007256249,0.0008343986,0.00004928521,0.00002709651,0.0001671132,0.0422122],"genre_scores_gemma":[0.3563917,0.004934629,0.5432464,0.0005897554,0.0008415708,0.000252168,0.0001370015,0.0005396096,0.09306708],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002318695,"threshold_uncertainty_score":0.007756829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05595085729039391,"score_gpt":0.2638236754132083,"score_spread":0.2078728181228144,"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."}}