{"id":"W2043815117","doi":"10.1145/1463434.1463520","title":"Optimal incremental multi-step nearest-neighbor search","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; k-nearest neighbors algorithm; Process (computing); Set (abstract data type); Algorithm; Nearest-neighbor chain algorithm; Feature (linguistics); Nearest neighbor search; Best bin first; Artificial intelligence; Cluster analysis","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001480361,0.0001324129,0.0001303347,0.00008964881,0.0002155182,0.00006460588,0.0007625305,0.00004943564,0.00009506299],"category_scores_gemma":[0.00003586594,0.0001121592,0.00006208972,0.0003566265,0.00009743269,0.0008929958,0.000500095,0.0001681583,0.0001999875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000486398,"about_ca_system_score_gemma":0.00006548117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001040279,"about_ca_topic_score_gemma":0.000003192241,"domain_scores_codex":[0.9987579,0.0000407968,0.0001767922,0.0003575356,0.0003253693,0.0003415816],"domain_scores_gemma":[0.9992419,0.00005051725,0.00002780665,0.0004610412,0.00009439114,0.0001242794],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001734854,0.001991904,0.03897111,0.0000867717,0.0001456281,0.00283771,0.004280582,0.0003477031,0.1114102,0.0606686,0.05016768,0.7289187],"study_design_scores_gemma":[0.001139853,0.0005425648,0.0118937,0.0000196871,0.000003486542,0.0003423479,0.00008366707,0.1773602,0.7908117,0.000125528,0.01705743,0.0006198824],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02294677,0.00008390041,0.9724544,0.0001843466,0.00005689984,0.000183743,0.000001149164,0.0006378718,0.00345097],"genre_scores_gemma":[0.2983122,0.00007212633,0.6993332,0.0003310924,0.00003266769,0.000008042176,0.000001504249,0.000008538595,0.001900688],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7282988,"threshold_uncertainty_score":0.457372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05841050307307297,"score_gpt":0.323476103225885,"score_spread":0.2650656001528121,"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."}}