{"id":"W2171754572","doi":"10.1109/hpcs.2007.10","title":"Benefits of Clustering in Landmark-Aided Positioning Algorithms","year":2007,"lang":"en","type":"article","venue":"","topic":"Peer-to-Peer Network Technologies","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; PlanetLab; Implementation; Overhead (engineering); Cluster analysis; Denial-of-service attack; Distributed computing; Algorithm; Computer network; Artificial intelligence; The Internet","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":[0.0005048215,0.00008307374,0.0001319819,0.0002933648,0.00002910598,0.00003569547,0.0006735584,0.00006345348,0.000003828889],"category_scores_gemma":[0.00004083759,0.00007966143,0.00002124873,0.0007689527,0.00001729663,0.0002127416,0.000508941,0.00009481075,0.00001136819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004523004,"about_ca_system_score_gemma":0.00001066114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001287051,"about_ca_topic_score_gemma":0.0008987642,"domain_scores_codex":[0.9990406,0.000009345446,0.0002417739,0.0002273317,0.0001823058,0.0002986235],"domain_scores_gemma":[0.9994271,0.00008883768,0.00004463686,0.0003510436,0.00004695135,0.00004137221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00001893653,0.00008920571,0.01979197,0.00002106432,0.00001684549,0.00004863699,0.001075954,0.03889881,0.003197046,0.05051114,0.0007385365,0.8855919],"study_design_scores_gemma":[0.001441472,0.000439869,0.6261636,0.0004332809,0.000006077241,0.0001087026,0.0003309507,0.2540901,0.1073902,0.007912242,0.0007852769,0.000898139],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2423349,0.0000632171,0.7507623,0.0006835922,0.0001271368,0.00008701033,5.037934e-7,0.0002926196,0.005648704],"genre_scores_gemma":[0.6914581,0.000003292935,0.3082815,0.0001378698,0.00001732695,0.000002566353,5.27626e-7,0.000003645271,0.00009521623],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8846937,"threshold_uncertainty_score":0.3248499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01739315724294304,"score_gpt":0.2561848218056776,"score_spread":0.2387916645627346,"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."}}