{"id":"W2168618447","doi":"10.1109/tvt.2007.904547","title":"Efficiency and Goodput Analysis of Dly-ACK in IEEE 802.15.3","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Goodput; Computer science; Markov chain; Transmission (telecommunications); Electronic engineering; Channel (broadcasting); Wireless; Computer network; Algorithm; Throughput; Engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002947197,0.0009206495,0.0008635456,0.0009787369,0.000567183,0.001167794,0.0009814024,0.0005138262,0.001374302],"category_scores_gemma":[0.007069159,0.0003473349,0.0005341089,0.0008594753,0.001247397,0.001458899,0.0007518066,0.000732303,0.0001977595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002727915,"about_ca_system_score_gemma":0.001077331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008167068,"about_ca_topic_score_gemma":0.004386797,"domain_scores_codex":[0.9982774,0.0006626634,0.00006828218,0.0001519937,0.0005213059,0.0003182531],"domain_scores_gemma":[0.9931173,0.005007016,0.0006272169,0.0003531491,0.0008124993,0.00008280152],"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":[0.0008997241,0.0001768329,0.006199368,0.0001943054,0.0001134229,0.0003453154,0.0003821307,0.9118567,0.01992664,0.03363319,0.001292513,0.02497991],"study_design_scores_gemma":[0.000006897083,0.00005686113,0.000664305,0.000007579474,0.00001893126,0.00005318495,0.00003112184,0.9930803,0.004291974,0.0016413,0.0001357053,0.00001180586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5893444,0.002581448,0.3994425,0.0005398177,0.00008122595,0.0001027159,0.0002122827,0.0009661504,0.006729451],"genre_scores_gemma":[0.9937675,0.0003394436,0.00521184,0.00002913031,0.00001300474,0.00001615517,0.00002946819,0.000036942,0.0005565446],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008167068,"threshold_uncertainty_score":0.0197925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004732631084200243,"score_gpt":0.2162806797728722,"score_spread":0.2115480486886719,"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."}}