{"id":"W4285075493","doi":"10.48550/arxiv.1907.07441","title":"Maximum rectilinear convex subsets","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Theory Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agencia Estatal de Investigación; Departamento de Investigaciones Científicas y Tecnológicas, Universidad de Santiago de Chile; Comisión Nacional de Investigación Científica y Tecnológica; European Commission; Natural Sciences and Engineering Research Council of Canada; Ministerio de Ciencia e Innovación; Gobierno de Aragón; Universidad de Santiago de Chile","keywords":"Convex hull; Combinatorics; Mathematics; Orthogonal convex hull; Regular polygon; Convex set; Boundary (topology); Convex polytope; Hull; Convex body; Point (geometry); Plane (geometry); Mixed volume; Convex combination; Interior point method; Geometry; Mathematical analysis; Algorithm; Convex optimization; Materials science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005501054,0.0009192322,0.0008869421,0.001067058,0.0006449758,0.001390417,0.001151432,0.0005761017,0.007556458],"category_scores_gemma":[0.003607671,0.000599096,0.0008564346,0.001821749,0.0007673205,0.002918936,0.001684356,0.001139824,0.001176084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007658078,"about_ca_system_score_gemma":0.0004653379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001610518,"about_ca_topic_score_gemma":0.00232539,"domain_scores_codex":[0.9991419,0.0001963504,0.00005124379,0.0002539607,0.0002422665,0.0001142659],"domain_scores_gemma":[0.9988368,0.0004935491,0.0001359378,0.0003216106,0.0001319568,0.00008007788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006672174,0.0001546308,0.003189928,0.000615461,0.0001182983,0.0003795716,0.0006076439,0.2936206,0.01625338,0.3659784,0.02088553,0.2975293],"study_design_scores_gemma":[0.0001101531,0.0001305191,0.001932148,0.00009366878,0.00004173614,0.0004003284,0.0003181029,0.6006408,0.01070227,0.3459099,0.03968766,0.00003270701],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1750819,0.001100297,0.7751343,0.001154014,0.0001102628,0.0002731737,0.002225298,0.001000585,0.04392027],"genre_scores_gemma":[0.4476714,0.0009277358,0.5281867,0.0002044483,0.0001156506,0.000297158,0.003704527,0.0003025168,0.0185898],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007556458,"threshold_uncertainty_score":0.02527893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08574779962800823,"score_gpt":0.2210373313479814,"score_spread":0.1352895317199732,"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."}}