{"id":"W2160696604","doi":"10.1142/s0218654309001197","title":"CONTOUR-BASED 3D POINT DATA SIMPLIFICATION FOR FREEFORM SURFACE RECONSTRUCTION","year":2009,"lang":"en","type":"article","venue":"International Journal of Shape Modeling","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Point cloud; Surface reconstruction; Reverse engineering; Surface (topology); Computer science; Spline (mechanical); Data point; Computer vision; Point (geometry); Algorithm; Artificial intelligence; B-spline; Control point; Data reduction; Development (topology); Process (computing); Computer graphics (images); Geometry; Mathematics; Data mining; Engineering; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005170295,0.0001275924,0.0002018097,0.0001912666,0.0000480265,0.00009536733,0.0005933752,0.00006812424,0.00003982393],"category_scores_gemma":[0.00009202744,0.0001212859,0.0001297697,0.00007231596,0.000009732781,0.0005576184,0.00001562725,0.0001721743,0.000004865831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001436325,"about_ca_system_score_gemma":0.00005369133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006775132,"about_ca_topic_score_gemma":0.000004493494,"domain_scores_codex":[0.9987136,0.00001038831,0.0006379746,0.0001496297,0.0003433398,0.0001450258],"domain_scores_gemma":[0.9987894,0.00005190129,0.0001819113,0.0002099265,0.0006986212,0.0000682225],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000424049,0.00002181686,0.00002716235,0.000005657507,0.0001034289,0.00000217723,0.00003691031,0.9201233,0.004406063,0.000032168,0.0001676242,0.07503123],"study_design_scores_gemma":[0.000599902,0.00003803132,0.0000101571,0.0001084518,0.0000591682,0.00004017994,0.00006437666,0.9968531,0.0006556928,0.001319077,0.000125088,0.0001267373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1606124,0.0002843426,0.8372394,0.0009527713,0.0005656318,0.00005478367,0.00005029976,0.00004501831,0.000195345],"genre_scores_gemma":[0.9547437,0.00009754777,0.04438598,0.0001446448,0.0005253842,7.577523e-7,0.00007505348,0.00001742313,0.000009526213],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7941313,"threshold_uncertainty_score":0.4945895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04998049288018395,"score_gpt":0.2887879607139566,"score_spread":0.2388074678337726,"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."}}