{"id":"W2816890","doi":"10.1007/978-3-642-23719-5_8","title":"Can Nearest Neighbor Searching Be Simple and Always Fast?","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Computational Geometry and Mesh Generation","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Voronoi diagram; Computer science; Point location; Delaunay triangulation; Logarithm; k-nearest neighbors algorithm; Algorithm; Simple (philosophy); Nearest neighbor search; Predicate (mathematical logic); Computational geometry; Theoretical computer science; Point (geometry); Data mining; Mathematics; Artificial intelligence","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":[0.002147848,0.0008438323,0.001478113,0.0009874349,0.001160503,0.002734266,0.002371938,0.002285416,0.01708963],"category_scores_gemma":[0.03087453,0.0009037714,0.0006443899,0.002118861,0.001270182,0.01518086,0.002311625,0.001606392,0.01406419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005093315,"about_ca_system_score_gemma":0.0008114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002783881,"about_ca_topic_score_gemma":0.003193172,"domain_scores_codex":[0.9983792,0.0002967636,0.00008715776,0.0003876546,0.0006730199,0.0001762189],"domain_scores_gemma":[0.9917436,0.003927398,0.000292438,0.002473969,0.001378658,0.000183918],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007263766,0.00009892104,0.001372371,0.0006270566,0.00008230418,0.0001532512,0.0002023205,0.01187567,0.005784161,0.1032668,0.07733141,0.7984793],"study_design_scores_gemma":[0.0001553871,0.0001560353,0.001532872,0.0003218106,0.0001176778,0.001263251,0.0006270809,0.1259347,0.01213366,0.7502418,0.1073872,0.0001284701],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02817439,0.008927304,0.8923782,0.008196758,0.003320695,0.0001157805,0.0007960549,0.006129061,0.05196191],"genre_scores_gemma":[0.2247227,0.007315013,0.7287624,0.001489543,0.001112767,0.0001624346,0.001594868,0.001706652,0.03313353],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01708963,"threshold_uncertainty_score":0.05717051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02834684279229392,"score_gpt":0.253094582071666,"score_spread":0.2247477392793721,"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."}}