{"id":"W2137996775","doi":"10.1109/tce.2008.4711219","title":"Three-dimensional absorbing Markov chain model for video streaming over IEEE 802.11 wireless networks","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Consumer Electronics","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Network packet; Computer network; Forward error correction; Automatic repeat request; Markov chain; Transmission (telecommunications); Overhead (engineering); Video quality; Real-time computing; Wireless network; Markov model; Channel (broadcasting); Error detection and correction; Markov process; Hybrid automatic repeat request; Wireless; Algorithm; Decoding methods; Telecommunications; Telecommunications link; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003848936,0.0004683895,0.0004543279,0.0001921525,0.0009733897,0.0001213411,0.0007442278,0.0002626787,0.00002260102],"category_scores_gemma":[0.000002891413,0.0004810656,0.0003262727,0.000513047,0.0001217515,0.0004901791,0.000007592958,0.000736054,0.00001122772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002444225,"about_ca_system_score_gemma":0.0005635261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008697427,"about_ca_topic_score_gemma":0.001314658,"domain_scores_codex":[0.9968809,0.00007154447,0.0005350414,0.0008666867,0.0004733845,0.001172461],"domain_scores_gemma":[0.9980969,0.0005143795,0.0001915656,0.0008064978,0.0001685969,0.0002220133],"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":[0.0001587635,0.0002574912,0.00007081261,0.00002337529,0.0001394676,0.00001648641,0.0001074529,0.9154034,0.0004782001,0.00128516,0.001857157,0.08020221],"study_design_scores_gemma":[0.001315578,0.0001754163,0.00003407084,0.00008997883,0.00003952995,0.00006543362,0.000001487359,0.9937155,0.002605417,0.0005503386,0.0008607656,0.0005465412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03225895,0.0004209168,0.9632808,0.0001481907,0.0006024779,0.002931808,0.00002359313,0.0003078748,0.00002538965],"genre_scores_gemma":[0.9783296,0.0001627206,0.01747495,0.0007153591,0.000139485,0.002805887,0.000005835948,0.00008341032,0.0002827221],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9460707,"threshold_uncertainty_score":0.9997641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02213232155585877,"score_gpt":0.2497141744298329,"score_spread":0.2275818528739741,"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."}}