{"id":"W2010369728","doi":"10.1007/s00453-004-1101-6","title":"A Linear-Time Approximation Scheme for Maximum Weight Triangulation of Convex Polygons","year":2004,"lang":"en","type":"article","venue":"Algorithmica","topic":"Computational Geometry and Mesh Generation","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Triangulation; Theory of computation; Polygon (computer graphics); Mathematics; Regular polygon; Minimum-weight triangulation; Convex polygon; Scheme (mathematics); Polygon covering; Delaunay triangulation; Combinatorics; Approximation algorithm; Convex combination; Algorithm; Mathematical optimization; Convex optimization; Computer science; Bowyer–Watson algorithm; Geometry; Mathematical analysis","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":[0.0002811042,0.00009743375,0.0001490822,0.0001542733,0.00008846142,0.0000315904,0.0002272346,0.0000646616,0.00001081329],"category_scores_gemma":[0.00007514116,0.00009859366,0.00008288579,0.0004419199,0.00002391155,0.0003585787,0.00004492947,0.00004637849,0.00003235451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004215671,"about_ca_system_score_gemma":0.0001274901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006803166,"about_ca_topic_score_gemma":4.90863e-7,"domain_scores_codex":[0.9990906,0.0000276511,0.0002815454,0.0002427907,0.0002097774,0.0001476745],"domain_scores_gemma":[0.9992922,0.00008313848,0.0001427997,0.0002190687,0.000211598,0.00005118014],"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":[0.0001225866,0.0007234294,0.00003395515,0.0001443968,0.0001416422,0.000003836557,0.00139599,0.02642408,0.1333401,0.3655921,0.001121586,0.4709563],"study_design_scores_gemma":[0.001585238,0.0001420448,0.0002751589,0.00002020406,0.00001123617,0.000009555471,0.000005064685,0.8213245,0.1062016,0.0685033,0.001765695,0.0001564368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01243272,0.00006701872,0.9852569,0.001393579,0.0002320398,0.0003686491,0.000011482,0.0000780708,0.0001595816],"genre_scores_gemma":[0.2682808,0.000002072027,0.7310416,0.0001111448,0.0002433922,0.0000395153,0.00008969159,0.000008116159,0.0001836824],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7949004,"threshold_uncertainty_score":0.4020533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01231174919954155,"score_gpt":0.2467655707140297,"score_spread":0.2344538215144882,"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."}}