{"id":"W1986752736","doi":"10.1117/12.417239","title":"<title>Adaptive reconstruction of closed Bezier surfaces</title>","year":2001,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced Numerical Analysis Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Bézier curve; Basis function; Bernstein polynomial; Artificial neural network; Polynomial; Surface (topology); Nonlinear system; Computer science; Parametric statistics; Network architecture; Basis (linear algebra); Topology (electrical circuits); Radial basis function; Parametric surface; Bézier surface; Backpropagation; Function (biology); Algorithm; Mathematics; Applied mathematics; Mathematical analysis; Geometry; Artificial intelligence; Combinatorics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001750525,0.0001904925,0.0003004424,0.0000879331,0.00002629021,0.00002054775,0.0003580895,0.0001413743,0.0001158738],"category_scores_gemma":[0.0001244485,0.0001681573,0.0003488071,0.0003174854,0.0001417722,0.0002338438,0.00005155927,0.0001893079,0.00001122096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001152917,"about_ca_system_score_gemma":0.00001042352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000160792,"about_ca_topic_score_gemma":8.617695e-8,"domain_scores_codex":[0.9988476,1.335984e-8,0.0004150401,0.0001888946,0.00033873,0.0002097146],"domain_scores_gemma":[0.9991,0.00005107528,0.0001580011,0.00004706498,0.0005836422,0.00006024592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004309294,0.00006837055,0.0001682968,0.0002532247,0.0005786358,1.66288e-7,0.00006816837,0.0007495538,0.69016,0.2879107,0.01201445,0.007985353],"study_design_scores_gemma":[0.001293442,0.0004971038,0.0009889678,0.0007310905,0.0005093075,0.00006233493,0.0005294006,0.3242096,0.5114186,0.01691944,0.1415843,0.001256492],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9530176,0.0001925252,0.0006281345,0.0001919753,0.0002056934,0.000209692,0.00002820107,0.0001893495,0.04533682],"genre_scores_gemma":[0.7712214,0.0006310401,0.2264143,0.0000305262,0.0003873566,0.00006409909,0.00001110794,0.0001001378,0.001140023],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.32346,"threshold_uncertainty_score":0.6857257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009963647802593808,"score_gpt":0.2231143005339936,"score_spread":0.2131506527313998,"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."}}