{"id":"W2486128631","doi":"10.1007/978-3-319-25388-6_2","title":"The nearest neighbor distance","year":2015,"lang":"en","type":"book-chapter","venue":"Springer series in the data sciences","topic":"Computational Geometry and Mesh Generation","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Equidistant; k-nearest neighbors algorithm; Combinatorics; Physics; Mathematics; Condensed matter physics; Geometry; 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":["scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.002793141,0.0001892686,0.0001410673,0.00008467164,0.0007278461,0.001158465,0.008502606,0.00006100932,0.00001270534],"category_scores_gemma":[0.0001835459,0.0001055227,0.00002987515,0.0003114354,0.0007267539,0.001397323,0.001767097,0.0002527347,0.00006462055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003490205,"about_ca_system_score_gemma":0.0003588493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001718766,"about_ca_topic_score_gemma":0.0005029725,"domain_scores_codex":[0.997865,0.00006219675,0.000277799,0.000627508,0.000907075,0.0002604378],"domain_scores_gemma":[0.9975514,0.0003465863,0.000176074,0.001791922,0.00009011578,0.0000438542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00000360208,0.00000435655,0.000008435041,0.000003849108,0.000004178569,0.000007402113,0.0001920848,0.0003070287,0.000001050342,0.9680005,0.01178967,0.01967779],"study_design_scores_gemma":[0.00003649711,0.00004316099,0.0001019413,0.00002722882,0.000003457337,0.00001688393,0.00004322719,0.005970193,0.000003532629,0.1380792,0.855523,0.0001517143],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00008339887,0.02135155,0.1041089,0.02624957,0.007270769,0.0009897611,0.0004078452,0.0001956698,0.8393425],"genre_scores_gemma":[0.07488471,0.008271364,0.1066629,0.004569303,0.006127656,0.0001675589,0.001224907,0.0001219905,0.7979696],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.8437333,"threshold_uncertainty_score":0.9998784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1087544459164901,"score_gpt":0.3074979761493724,"score_spread":0.1987435302328823,"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."}}