{"id":"W2176441584","doi":"10.1109/pacrim.2015.7334826","title":"The Area Code Tree for nearest neighbour searching","year":2015,"lang":"en","type":"article","venue":"","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; Code (set theory); Nearest neighbour; Trie; Tree (set theory); Sequence (biology); k-nearest neighbors algorithm; Nearest neighbor search; Chain code; Algorithm; Theoretical computer science; Data structure; Data mining; Artificial intelligence; Mathematics; Combinatorics; 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":[0.0007472652,0.0004330344,0.0008259624,0.002113247,0.001235746,0.001617575,0.001764963,0.0009999541,0.004964555],"category_scores_gemma":[0.007644495,0.0003132922,0.0005964268,0.003843011,0.0007867579,0.003406134,0.001844207,0.0009562593,0.003016957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008747418,"about_ca_system_score_gemma":0.0015152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007168413,"about_ca_topic_score_gemma":0.005691546,"domain_scores_codex":[0.9982546,0.0002662337,0.0001111448,0.0002410176,0.0009983768,0.0001285459],"domain_scores_gemma":[0.9969863,0.0009083577,0.0001786936,0.0006588554,0.001152231,0.0001154988],"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":[0.0003803333,0.000116937,0.003162127,0.0003585339,0.00007443033,0.0002594383,0.0004128735,0.07664861,0.01012544,0.09708223,0.02475362,0.7866255],"study_design_scores_gemma":[0.00007736881,0.0002557361,0.001572894,0.0001529128,0.00005440254,0.001011911,0.0003200327,0.7883914,0.01218924,0.1136191,0.08224487,0.0001102298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01016012,0.00111797,0.9799013,0.0002472551,0.0001294342,0.0001208957,0.0004246026,0.00175573,0.006142711],"genre_scores_gemma":[0.1791285,0.001184514,0.8114535,0.0002501004,0.0001163851,0.0003115016,0.002086691,0.0003480581,0.005120752],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007168413,"threshold_uncertainty_score":0.01660812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09276382512785833,"score_gpt":0.2980709151642166,"score_spread":0.2053070900363582,"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."}}