{"id":"W1540196280","doi":"10.1007/978-1-4939-2864-4_256","title":"Nearest Neighbor Interchange and Related Distances","year":2016,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Algorithms","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"k-nearest neighbors algorithm; Geography; Computer science; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002672341,0.0004638081,0.0005555117,0.000292522,0.00008448868,0.0001228517,0.001387201,0.0002509113,0.0002968035],"category_scores_gemma":[0.00002949839,0.000366775,0.0001408542,0.000090639,0.0002810918,0.0008831453,0.00115594,0.0002959285,0.0001960523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000416582,"about_ca_system_score_gemma":0.0000542948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001056479,"about_ca_topic_score_gemma":0.000009236856,"domain_scores_codex":[0.9977002,0.00002391835,0.0005860442,0.0008343039,0.0004937941,0.000361758],"domain_scores_gemma":[0.9981986,0.0001464855,0.000437743,0.0009568112,0.00009778448,0.0001625758],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000004403863,0.00001740393,0.0000103983,0.00007018793,0.0001074679,0.0001005195,0.0002613047,1.233693e-7,0.000001748831,0.3197005,0.005022177,0.6747038],"study_design_scores_gemma":[0.0005031223,0.0002020237,0.0001156702,0.0005092526,0.00005937271,0.00001745325,0.00001135928,0.00123659,0.000006836809,0.07378496,0.9228688,0.0006845621],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.000007589083,0.002877461,0.03389673,0.0007271108,0.00200181,0.0003677708,0.0002290022,0.0001926456,0.9596999],"genre_scores_gemma":[0.0001114227,0.01442428,0.02316871,0.00008083305,0.0005415311,0.00001861564,0.00007453134,0.00007133275,0.9615088],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.9178466,"threshold_uncertainty_score":0.9998784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01183536237195373,"score_gpt":0.2198466465612166,"score_spread":0.2080112841892629,"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."}}