{"id":"W4405112553","doi":"10.1016/j.procs.2024.11.088","title":"Evaluating Safe Region Sizes for Accuracy in Approximate Continuous Nearest Neighbour Queries","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Computer science; Nearest neighbour; k-nearest neighbors algorithm; Data mining; Artificial intelligence; Pattern recognition (psychology); Information retrieval","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":[0.009403248,0.001037469,0.001700499,0.002480603,0.00103442,0.002781322,0.002730346,0.001802486,0.001166065],"category_scores_gemma":[0.1122807,0.0004197623,0.0006311794,0.002770768,0.001345031,0.005134637,0.002093786,0.001078708,0.0004302553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002399972,"about_ca_system_score_gemma":0.002113494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01078906,"about_ca_topic_score_gemma":0.006233548,"domain_scores_codex":[0.9820237,0.005048257,0.001390532,0.001742708,0.00888845,0.0009063945],"domain_scores_gemma":[0.8744162,0.09615579,0.006685711,0.009373214,0.01226138,0.001107688],"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":[0.003433154,0.0003728269,0.02382765,0.000512754,0.000272691,0.0002874456,0.0007374132,0.7649071,0.01226443,0.008003461,0.00266857,0.1827125],"study_design_scores_gemma":[0.00004618203,0.0006581416,0.004401048,0.00003759517,0.0000525285,0.0003074737,0.000432976,0.9784387,0.01188482,0.00271249,0.000968082,0.00005994756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4879162,0.00677283,0.4934461,0.0007019395,0.0002741865,0.0005944576,0.001043788,0.002882235,0.006368362],"genre_scores_gemma":[0.9132884,0.0006006098,0.08484516,0.0000623231,0.00004640367,0.0001084529,0.0005671501,0.000130717,0.0003507817],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01078906,"threshold_uncertainty_score":0.0497297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04999562638153934,"score_gpt":0.3355169866006814,"score_spread":0.285521360219142,"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."}}