{"id":"W2140701037","doi":"10.1109/vetecs.2011.5956252","title":"Range-Based Localization in Wireless Networks Using the DBSCAN Clustering Algorithm","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"DBSCAN; Computer science; Algorithm; Cluster analysis; Range (aeronautics); Intersection (aeronautics); Metric (unit); Euclidean distance; Singular value decomposition; Artificial intelligence; CURE data clustering algorithm; Correlation clustering","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":[],"consensus_categories":[],"category_scores_codex":[0.0001168398,0.0001177898,0.0001082934,0.0001095375,0.0000628598,0.00002346488,0.0001634628,0.0001172142,0.00006959624],"category_scores_gemma":[0.000006061267,0.00009018543,0.00002843897,0.0004066853,0.00004854589,0.00008446719,0.00002781069,0.0001244884,0.000003099536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006927445,"about_ca_system_score_gemma":0.000008713264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002031895,"about_ca_topic_score_gemma":0.0002646819,"domain_scores_codex":[0.9993793,0.0000203187,0.0001909453,0.0001050054,0.00008114817,0.000223235],"domain_scores_gemma":[0.9997287,0.00001848879,0.00001970983,0.0001884896,0.00002542787,0.00001918115],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003472729,0.000008120565,0.002867757,0.00001387163,0.000005931456,0.00000397288,0.0001875577,0.9759908,0.00003564367,0.0003262117,0.00006780781,0.02048884],"study_design_scores_gemma":[0.0002380545,0.000006852888,0.0002746456,0.00002591702,0.000005329091,0.000001835769,0.000238503,0.9936605,0.005262801,0.00006342687,0.00009394512,0.0001281967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008527645,0.00007661652,0.9887921,0.00001105668,0.0002387216,0.0001457912,7.970898e-7,0.000606416,0.001600911],"genre_scores_gemma":[0.9938174,0.00002388216,0.005942632,0.0001238855,0.00003377559,0.00001260961,0.000004313826,0.00002992473,0.00001151358],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9852898,"threshold_uncertainty_score":0.3677655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02156308823529762,"score_gpt":0.2086528816801599,"score_spread":0.1870897934448623,"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."}}