{"id":"W2773875520","doi":"10.20382/jocg.v9i1a8","title":"Array-based compact data structures for triangulations: Practical solutions with theoretical guarantees","year":2017,"lang":"en","type":"preprint","venue":"Journal of Computational Geometry (Carleton University)","topic":"Computational Geometry and Mesh Generation","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agence Nationale de la Recherche","keywords":"Computer science; Distributed computing; Mathematical optimization; Theoretical computer science; Algorithm; Computational science; Topology (electrical circuits); Mathematics; Combinatorics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001164782,0.0003830366,0.0006718689,0.00174259,0.0007434073,0.0007101864,0.002783124,0.0002718547,0.00003894507],"category_scores_gemma":[0.0007874458,0.0003531207,0.0003452826,0.0006806855,0.0003704673,0.001397773,0.0005844717,0.0007958824,0.000002998259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002467986,"about_ca_system_score_gemma":0.003663664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008383801,"about_ca_topic_score_gemma":0.000005962849,"domain_scores_codex":[0.9968303,0.0003047244,0.0006676365,0.0006703563,0.001164432,0.0003625004],"domain_scores_gemma":[0.9926482,0.002023368,0.001794163,0.00107695,0.002198689,0.000258627],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003971031,0.0001978206,0.0001115626,0.00006895783,0.0003765476,0.00007431514,0.00005168582,0.6811835,0.00002095135,0.3128666,0.002951849,0.001699196],"study_design_scores_gemma":[0.004762011,0.0008076054,0.007252589,0.0003650608,0.0006139821,0.0003974043,0.00009920933,0.7059001,0.0001436606,0.2555863,0.02324439,0.0008277033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006383232,0.0002140587,0.9839032,0.007216506,0.0009529592,0.0004736065,0.0003581611,0.0000533807,0.0004448832],"genre_scores_gemma":[0.6073247,0.00001620189,0.3914092,0.0001471731,0.000493011,6.922366e-7,0.0005154528,0.0000175878,0.00007598422],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6009415,"threshold_uncertainty_score":0.9998921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07517421534455519,"score_gpt":0.3201279061553591,"score_spread":0.2449536908108039,"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."}}