{"id":"W1586963669","doi":"10.1007/978-3-540-76725-1_28","title":"Smooth Image Surface Approximation by Piecewise Cubic Polynomials","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Numerical Analysis Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Piecewise; Subdivision surface; Triangulation; Surface (topology); Computer science; Computer graphics; Algorithm; Data point; Image (mathematics); Artificial intelligence; Mathematics; Geometry; Computer graphics (images); Mathematical analysis; Polygon mesh","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":[0.0001589381,0.0005430068,0.0006188662,0.0007132299,0.0001782317,0.001147409,0.0005794396,0.0004511366,0.003551702],"category_scores_gemma":[0.0008890328,0.0003417113,0.000484652,0.0008870744,0.000505805,0.0008504494,0.0008208198,0.001507249,0.001299717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000475036,"about_ca_system_score_gemma":0.0002498847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001489791,"about_ca_topic_score_gemma":0.001058108,"domain_scores_codex":[0.9998672,0.00001618023,0.000003808378,0.00001724178,0.00007922742,0.00001632797],"domain_scores_gemma":[0.9998555,0.0000508789,0.00001008339,0.00003381404,0.00003761064,0.00001195075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001897296,0.00005101406,0.0003831711,0.0003317379,0.00004155471,0.0001791968,0.0002033484,0.2247466,0.05806795,0.2918666,0.01068519,0.4132538],"study_design_scores_gemma":[0.00000993303,0.00003256313,0.0001579078,0.00002088467,0.00001237181,0.0002205477,0.00003162773,0.9310229,0.00743811,0.04641249,0.01462629,0.00001428148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01788033,0.0007411213,0.9665258,0.0001083984,0.00009321457,0.00001819237,0.00005936878,0.0006448702,0.01392874],"genre_scores_gemma":[0.4386678,0.003097033,0.5237826,0.00008577748,0.000141643,0.00004456754,0.0003155329,0.0008394605,0.03302564],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003551702,"threshold_uncertainty_score":0.01188159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01088071311298779,"score_gpt":0.2483717229047413,"score_spread":0.2374910097917535,"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."}}