{"id":"W2146968416","doi":"10.1109/rawcon.2003.1227934","title":"Reducing collisions between bluetoioth piconets by orthogonal hop set partitioning","year":2004,"lang":"en","type":"article","venue":"","topic":"Bluetooth and Wireless Communication Technologies","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Piconet; Hop (telecommunications); Bluetooth; Computer science; Computer network; Set (abstract data type); Algorithm; Wireless; Telecommunications","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":[0.0001959143,0.0001304231,0.0001635551,0.0001071522,0.0003534571,0.0002099157,0.001310318,0.0001097408,0.00002018909],"category_scores_gemma":[0.00007409838,0.0001197051,0.00005183715,0.0005354021,0.0001062117,0.0005506574,0.0004930145,0.0002315155,0.00008659166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005685896,"about_ca_system_score_gemma":0.0001169211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001032251,"about_ca_topic_score_gemma":0.00001622526,"domain_scores_codex":[0.9988385,0.00003728506,0.0002766156,0.0003215199,0.0002171315,0.0003088911],"domain_scores_gemma":[0.9986644,0.0001229641,0.00009653348,0.0009472269,0.00006534818,0.0001034601],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000004891253,0.0002460899,0.02131244,0.00002249846,0.00009531715,0.00001239979,0.002516621,0.004068635,0.004989918,0.8156565,0.0254194,0.1256552],"study_design_scores_gemma":[0.004722465,0.0007587958,0.03871467,0.0005262877,0.00005810482,0.00008426642,0.001980762,0.01529554,0.6373125,0.2039869,0.09359942,0.002960292],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4235336,0.000337799,0.5604903,0.011496,0.00009172016,0.0001448265,0.00001755511,0.001306971,0.002581158],"genre_scores_gemma":[0.9377506,0.00008074996,0.06162508,0.0003408091,0.00002253845,0.00002356036,0.00002640137,0.000007528338,0.0001227483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6323226,"threshold_uncertainty_score":0.4881432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02946719735143463,"score_gpt":0.2693003038839772,"score_spread":0.2398331065325426,"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."}}