{"id":"W2339879713","doi":"10.1016/j.comgeo.2016.04.001","title":"Approximating the minimum closest pair distance and nearest neighbor distances of linearly moving points","year":2016,"lang":"en","type":"article","venue":"Computational Geometry","topic":"Computational Geometry and Mesh Generation","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"k-nearest neighbors algorithm; Mathematics; Nearest-neighbor chain algorithm; Combinatorics; Computer science; Artificial intelligence; Statistics","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":[0.001543868,0.0008161523,0.001309949,0.002127231,0.0006389353,0.001411102,0.002573864,0.001846101,0.002157918],"category_scores_gemma":[0.01490609,0.0007911669,0.000793102,0.002265265,0.0008452471,0.00220333,0.001868386,0.001525002,0.0008532879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001074466,"about_ca_system_score_gemma":0.0009107973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004933035,"about_ca_topic_score_gemma":0.005219157,"domain_scores_codex":[0.9986883,0.0003269233,0.00008418366,0.0002634921,0.0005566464,0.00008040273],"domain_scores_gemma":[0.9969613,0.001779263,0.0002249847,0.0003487075,0.0005856025,0.0001001606],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003257173,0.00009873162,0.002080568,0.0001688011,0.00004640769,0.0001083683,0.0001615357,0.8381531,0.002651274,0.02604944,0.002539779,0.1276162],"study_design_scores_gemma":[0.000008029876,0.00002164515,0.0001524668,0.00000866493,0.000004802107,0.0000325646,0.00002786198,0.9912952,0.001015631,0.006856031,0.0005687065,0.000008325466],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04994265,0.0005125762,0.9469523,0.0001443778,0.00009791681,0.00005436156,0.0001643684,0.0002715852,0.0018599],"genre_scores_gemma":[0.3950732,0.0003855605,0.6007016,0.0000423324,0.00005956381,0.0001515932,0.0005789133,0.0001775923,0.002829668],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004933035,"threshold_uncertainty_score":0.00980866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01216303096800642,"score_gpt":0.2329168146598109,"score_spread":0.2207537836918045,"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."}}