{"id":"W2149884111","doi":"","title":"Computing Nice Sweeps for Polyhedra and Polygons","year":2004,"lang":"en","type":"article","venue":"Canadian Conference on Computational Geometry","topic":"Computational Geometry and Mesh Generation","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Polyhedron; Monotone polygon; Combinatorics; Regular polygon; Polygon (computer graphics); Mathematics; Rectilinear polygon; Simple polygon; Convex polygon; Convex set; Polygon covering; Krein–Milman theorem; Convex polytope; Computer science; Geometry; Convex optimization","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":[0.00080415,0.001350292,0.001559193,0.001596354,0.0009885931,0.002211976,0.001978249,0.001649463,0.008006228],"category_scores_gemma":[0.006333788,0.001010616,0.002119581,0.001906889,0.002031208,0.005295067,0.002741669,0.001641296,0.001578557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009796768,"about_ca_system_score_gemma":0.0006737444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003100108,"about_ca_topic_score_gemma":0.005455537,"domain_scores_codex":[0.9986637,0.0001932479,0.0001369985,0.0003662496,0.0003911302,0.0002487272],"domain_scores_gemma":[0.997227,0.001574118,0.0002074377,0.0005712394,0.0002617367,0.0001584163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001390157,0.0003179331,0.01140498,0.0009971915,0.0001780666,0.0008591124,0.0008271842,0.5145707,0.01623433,0.1049339,0.01401576,0.3342707],"study_design_scores_gemma":[0.0001220585,0.0002003768,0.0008028168,0.00004899947,0.0000300508,0.0001840829,0.0004190931,0.8467352,0.006740134,0.1392852,0.005400006,0.0000319416],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2851427,0.0007128646,0.6915278,0.0005768563,0.0001079744,0.0002710843,0.002826605,0.008261706,0.01057244],"genre_scores_gemma":[0.4496189,0.0002421494,0.5392552,0.0001341325,0.00007989079,0.0001460808,0.00646723,0.0009176622,0.003138684],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008006228,"threshold_uncertainty_score":0.02678347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02848420590755417,"score_gpt":0.2638921882300127,"score_spread":0.2354079823224585,"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."}}