{"id":"W2932744841","doi":"10.4230/lipics.socg.2019.52","title":"Dynamic Planar Point Location in External Memory","year":2019,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Computational Geometry and Mesh Generation","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Point location; Auxiliary memory; Binary logarithm; Data structure; Combinatorics; Log-log plot; Upper and lower bounds; Subdivision; Block size; Computer science; Point (geometry); Block (permutation group theory); Planar; Mathematics; Algorithm; Discrete mathematics; Geometry","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":[0.0007075112,0.0007627869,0.001255508,0.0008050642,0.0009362686,0.002324077,0.005536574,0.001415801,0.009225329],"category_scores_gemma":[0.004324776,0.0006409361,0.0008055814,0.002664698,0.001313695,0.008777648,0.007411817,0.001739739,0.002786144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001023994,"about_ca_system_score_gemma":0.001030146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002090271,"about_ca_topic_score_gemma":0.002244387,"domain_scores_codex":[0.9984179,0.0002147824,0.000117749,0.0004115762,0.000561847,0.0002761943],"domain_scores_gemma":[0.9970551,0.0005762205,0.0002035579,0.001773996,0.0002687044,0.0001224237],"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":[0.002206695,0.0003302385,0.004883144,0.0006744868,0.0001476979,0.0005449757,0.0005832678,0.355558,0.0308037,0.1788023,0.03573469,0.3897309],"study_design_scores_gemma":[0.000241,0.0003145365,0.0006652283,0.00006846716,0.00006161418,0.0004593778,0.0002759311,0.7884561,0.02531574,0.1514433,0.03262999,0.0000687979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08050033,0.0007162687,0.8996843,0.0007366908,0.000128473,0.0001197783,0.00135689,0.007257002,0.009500261],"genre_scores_gemma":[0.6240528,0.000609919,0.3593554,0.0004157767,0.0001253963,0.0004987094,0.004478228,0.0008935677,0.009570132],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009225329,"threshold_uncertainty_score":0.03086185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02223815142951363,"score_gpt":0.1671257718530224,"score_spread":0.1448876204235087,"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."}}