{"id":"W83853472","doi":"10.1007/978-3-642-29063-3_13","title":"Automated Traffic Route Identification Through the Shared Nearest Neighbour Algorithm","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in geoinformation and cartography","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Identification (biology); Cluster analysis; Position (finance); Computer science; Data mining; Noise (video); k-nearest neighbors algorithm; Range (aeronautics); Key (lock); Algorithm; Artificial intelligence; Engineering","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":[0.0003148283,0.000367699,0.0002679944,0.000391121,0.00025701,0.0009333619,0.0007909957,0.0002590839,0.00005496058],"category_scores_gemma":[0.00001606331,0.000279163,0.000157145,0.0003682342,0.0001050618,0.002342463,0.0002434645,0.0004069613,0.0000898937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003155131,"about_ca_system_score_gemma":0.00002780972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003035155,"about_ca_topic_score_gemma":0.00005190239,"domain_scores_codex":[0.9982815,0.00002966992,0.0005614254,0.0003380425,0.0004438693,0.0003454622],"domain_scores_gemma":[0.9986426,0.00008742166,0.0003772601,0.0007410269,0.00008624,0.00006542509],"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":[0.000002998119,0.0000142812,0.00001850894,0.00006431663,0.00005962654,0.000002895295,0.003101016,0.0008045267,0.000001469058,0.03333092,0.001527541,0.9610719],"study_design_scores_gemma":[0.0006572276,0.00005734207,0.00219353,0.0001633794,0.00009446034,0.00003028435,0.00003866549,0.5444038,0.00002700349,0.01572453,0.4357527,0.0008570247],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00005141128,0.003770651,0.9533754,0.001725179,0.001190658,0.001121363,0.0002140651,0.001041675,0.03750963],"genre_scores_gemma":[0.5412092,0.02228542,0.3391137,0.02796093,0.005173592,0.001026377,0.03168994,0.00066259,0.03087827],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9602149,"threshold_uncertainty_score":0.999966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01144326639115611,"score_gpt":0.2252938662345749,"score_spread":0.2138505998434188,"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."}}