{"id":"W1546244091","doi":"10.1109/vetecf.2004.1405091","title":"Performance of dynamic timeslot-code assignment strategies in UTRA-TDD","year":2005,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Transmission (telecommunications); Interference (communication); Base station; Real-time computing; Heuristic; Metric (unit); Computer network; Code (set theory); Signal-to-noise ratio (imaging); Telecommunications; Engineering; 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":[],"consensus_categories":[],"category_scores_codex":[0.0004291043,0.0000847261,0.0001258294,0.0001387382,0.00003997123,0.00007105414,0.00154497,0.00004306043,0.00007897167],"category_scores_gemma":[0.00000558793,0.00007536761,0.00002590015,0.0004262178,0.00006273109,0.0007316354,0.0003728145,0.0001802388,0.00007157488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001038882,"about_ca_system_score_gemma":0.000136375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002473255,"about_ca_topic_score_gemma":0.000222884,"domain_scores_codex":[0.9988492,0.00008901563,0.0002767108,0.0001939253,0.0003315025,0.0002596385],"domain_scores_gemma":[0.9988661,0.00009876893,0.00005684359,0.0008779307,0.00005308654,0.00004731031],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002513655,0.0005636535,0.00506093,0.00006453821,0.00002521858,0.000003369674,0.001473476,0.2337444,0.007562923,0.108459,0.001112479,0.6419049],"study_design_scores_gemma":[0.0001900262,0.00005102453,0.01681164,0.00002427983,4.05556e-7,0.000001758908,0.00004745564,0.9802117,0.001648664,0.0001532139,0.0007740462,0.00008577286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7359984,0.0003824858,0.1925304,0.003579054,0.00005205952,0.0003021901,0.000001009869,0.0001514478,0.067003],"genre_scores_gemma":[0.9574799,0.0002492554,0.04127912,0.00004874684,0.000008420874,0.00001822974,0.000001271839,0.000005374548,0.0009097132],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7464673,"threshold_uncertainty_score":0.3073402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01883498366678592,"score_gpt":0.2954705422555272,"score_spread":0.2766355585887413,"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."}}