{"id":"W4301413600","doi":"10.20382/jocg.v5i1a1","title":"Unions of onions: preprocessing imprecise points for fast onion decomposition","year":2014,"lang":"en","type":"preprint","venue":"Journal of Computational Geometry (Carleton University)","topic":"Computational Geometry and Mesh Generation","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Disjoint sets; Decomposition; Combinatorics; Mathematics; Matching (statistics); Disjoint union (topology); Binary logarithm; Time complexity; Regular polygon; Set (abstract data type); Point (geometry); Data structure; Discrete mathematics; Unit disk; Computer science; Chemistry; Statistics; Geometry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008797047,0.00176297,0.001797168,0.002480532,0.001181592,0.003041968,0.002890194,0.001458224,0.01102509],"category_scores_gemma":[0.007149363,0.001103746,0.002093489,0.003918825,0.001141204,0.005365756,0.005757827,0.002580978,0.004614145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001022567,"about_ca_system_score_gemma":0.001097496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003990701,"about_ca_topic_score_gemma":0.006530981,"domain_scores_codex":[0.9981608,0.0001787694,0.0002160559,0.0003628508,0.0007727482,0.0003088007],"domain_scores_gemma":[0.9965345,0.0009196921,0.0002345281,0.00160475,0.0005714672,0.000135208],"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":[0.0020297,0.0002686282,0.005864054,0.0007857647,0.0001712686,0.0006353875,0.001729947,0.1023714,0.03621034,0.05915628,0.04327312,0.7475041],"study_design_scores_gemma":[0.0001683286,0.0002200974,0.001478293,0.0001269863,0.00006180497,0.000366804,0.0009981279,0.8121123,0.04216183,0.1046964,0.03753004,0.00007899114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0364756,0.0003326487,0.9482235,0.0002776817,0.000101728,0.0001793482,0.001556127,0.008777685,0.004075661],"genre_scores_gemma":[0.1983501,0.0002864202,0.7860416,0.0001421045,0.00006857257,0.0003425225,0.007765668,0.002309103,0.004693932],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01102509,"threshold_uncertainty_score":0.03688264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01515668206177541,"score_gpt":0.2662926280989182,"score_spread":0.2511359460371428,"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."}}