{"id":"W2160650170","doi":"","title":"Cache-Oblivious Output-Sensitive Two-Dimensional Convex Hull","year":2007,"lang":"en","type":"article","venue":"Canadian Conference on Computational Geometry","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Convex hull; Cache; Cache-oblivious algorithm; Parallel computing; Combinatorics; Computer science; Cache algorithms; Block size; CPU cache; Algorithm; Mathematics; Regular polygon; Geometry; Key (lock)","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008239604,0.0002482391,0.0002418407,0.001191236,0.0003574781,0.0002995012,0.0006301748,0.0001202037,0.0004251027],"category_scores_gemma":[0.0001546093,0.0002670281,0.0000796156,0.001141487,0.0001499829,0.000312355,0.0001023974,0.0003742547,0.001271042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003471076,"about_ca_system_score_gemma":0.001960258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005117494,"about_ca_topic_score_gemma":0.008995122,"domain_scores_codex":[0.9974022,0.000103346,0.0003719928,0.0006392716,0.000735965,0.0007472186],"domain_scores_gemma":[0.997021,0.0005170401,0.000110352,0.0003574988,0.000921162,0.001072943],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000021097,0.0001058869,0.0006490031,0.00001157736,0.00006793236,0.0004553143,0.000585801,0.2456183,0.00004660781,0.6999415,0.009012252,0.04348474],"study_design_scores_gemma":[0.00128239,0.0001954255,0.01739549,0.00006466841,0.000007328268,0.000136904,0.0001133093,0.9554614,0.0001857817,0.01944844,0.004960803,0.0007480308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0283744,0.00003829154,0.8928823,0.007770827,0.001022924,0.000525817,0.00009343937,0.0002518029,0.06904016],"genre_scores_gemma":[0.9604566,0.000001538291,0.03050473,0.007022279,0.00009696586,0.000005891687,0.0001211901,0.00001695491,0.001773915],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9320821,"threshold_uncertainty_score":0.9999782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03535666633472642,"score_gpt":0.2783073321059049,"score_spread":0.2429506657711784,"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."}}