{"id":"W4390018095","doi":"10.1137/1.9781611977806.ch10","title":"Chapter 10: Normal Cones","year":2023,"lang":"en","type":"book-chapter","venue":"Society for Industrial and Applied Mathematics eBooks","topic":"Advanced Graph Theory Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Cone (formal languages); Combinatorics; Dual cone and polar cone; Convex cone; Mathematics; Regular polygon; Geometry; Convex set; Convex optimization; Algorithm","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":[0.0004095233,0.000962197,0.0004835639,0.001392249,0.001337048,0.003450244,0.0007974587,0.0006366701,0.06169859],"category_scores_gemma":[0.001034916,0.0003603923,0.0004972488,0.00152254,0.001331551,0.003716438,0.00112932,0.003233805,0.01836991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001748953,"about_ca_system_score_gemma":0.001320775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002511893,"about_ca_topic_score_gemma":0.002459228,"domain_scores_codex":[0.9994735,0.00006017392,0.00001895907,0.00009090038,0.0003112069,0.00004524915],"domain_scores_gemma":[0.9996152,0.00009462901,0.00001732372,0.00003319441,0.0002009277,0.00003872531],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001405576,0.00002023663,0.00004753516,0.0001662789,0.000002946528,0.00004283367,0.0001430565,0.0005499402,0.0008018463,0.7913047,0.1480971,0.05880943],"study_design_scores_gemma":[0.000002981611,0.00001218174,0.00008354845,0.0001506107,0.000002641352,0.0001470679,0.00007424422,0.0005449935,0.0004250184,0.2536591,0.7448878,0.000009827131],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.002101611,0.02065885,0.03911573,0.00289926,0.003442201,0.00007416317,0.000707171,0.0003230939,0.930678],"genre_scores_gemma":[0.04485158,0.04757281,0.03276882,0.002391953,0.004772526,0.0002144261,0.00207429,0.0009719509,0.8643817],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.06169859,"threshold_uncertainty_score":0.2064023,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1041440404875739,"score_gpt":0.2799835011464473,"score_spread":0.1758394606588733,"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."}}