{"id":"W1571741349","doi":"10.1023/a:1014310721543","title":"On a Nearest-Neighbor Problem Under Minkowski and Power Metrics for Large Data Sets","year":2002,"lang":"en","type":"article","venue":"The Journal of Supercomputing","topic":"Computational Geometry and Mesh Generation","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Voronoi diagram; Computer science; Metric (unit); Set (abstract data type); Data structure; Computational geometry; Binary logarithm; Algorithm; Online analytical processing; Preprocessor; Point (geometry); Theoretical computer science; Data mining; Data warehouse; Discrete mathematics; Mathematics; Programming language","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":[0.01266856,0.001736201,0.006368267,0.003807624,0.002558917,0.004353508,0.006388078,0.005034692,0.003313725],"category_scores_gemma":[0.07987721,0.001889915,0.002386879,0.006090108,0.004961071,0.01724664,0.00646856,0.003159091,0.0004435244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002735003,"about_ca_system_score_gemma":0.001592284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009141881,"about_ca_topic_score_gemma":0.005661529,"domain_scores_codex":[0.9940602,0.00261725,0.0006602335,0.001174526,0.001180527,0.0003071636],"domain_scores_gemma":[0.9206271,0.0678857,0.003315215,0.003675342,0.002899032,0.001597447],"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":[0.0005761822,0.0003424292,0.004067657,0.0008756902,0.0002934311,0.0004521429,0.0007419596,0.6842487,0.001045617,0.2040108,0.0102479,0.09309748],"study_design_scores_gemma":[0.00003657472,0.00004884004,0.0003361262,0.00002816253,0.00002491587,0.00008746841,0.00009360939,0.7870699,0.0001628563,0.2113904,0.0006977339,0.00002336255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1134421,0.003393235,0.8740185,0.005446027,0.0002716144,0.0001882316,0.0007510243,0.0002841824,0.002205072],"genre_scores_gemma":[0.4840619,0.004905075,0.4972579,0.0009047132,0.001681004,0.0004907253,0.00333315,0.0004861193,0.006879414],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01266856,"threshold_uncertainty_score":0.0669986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06344665619959859,"score_gpt":0.289068506177096,"score_spread":0.2256218499774973,"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."}}