{"id":"W2160152607","doi":"10.14778/1687627.1687754","title":"Efficient method for maximizing bichromatic reverse nearest neighbor","year":2009,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":129,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; k-nearest neighbors algorithm; Point (geometry); Best bin first; Exponential function; Algorithm; Exponential growth; Theoretical computer science; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00157206,0.0011313,0.001877277,0.002361787,0.0008605612,0.001022602,0.002674639,0.001167936,0.004400157],"category_scores_gemma":[0.006288352,0.0006356133,0.0008446203,0.002812432,0.0006000947,0.002264846,0.002824784,0.000913428,0.001803275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009242684,"about_ca_system_score_gemma":0.001659337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003892747,"about_ca_topic_score_gemma":0.006198694,"domain_scores_codex":[0.997642,0.0005515056,0.0001347962,0.0004520314,0.001029017,0.0001906925],"domain_scores_gemma":[0.9980293,0.0007052377,0.0001990311,0.0003853452,0.0006047926,0.00007631326],"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":[0.0004481573,0.0002318686,0.001836264,0.0003755607,0.00008333039,0.0001567217,0.0002793113,0.1528829,0.01591794,0.01922286,0.009857926,0.7987072],"study_design_scores_gemma":[0.00006724329,0.00009956697,0.0005798119,0.00002553458,0.00002845482,0.0003891127,0.000107585,0.9690306,0.01016738,0.01329293,0.006174479,0.0000373106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01342903,0.0005591736,0.982529,0.0001304045,0.00004309442,0.000119588,0.0001318259,0.001028171,0.00202968],"genre_scores_gemma":[0.09750411,0.0002296065,0.8994473,0.00008888402,0.00003843769,0.0002012013,0.000406396,0.0001908526,0.001893243],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004400157,"threshold_uncertainty_score":0.01472002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01458119393423405,"score_gpt":0.2572641271071092,"score_spread":0.2426829331728751,"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."}}