{"id":"W2388383141","doi":"","title":"An End to End Quality Optimized Error Control for Wireless Video Transmission","year":2011,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Video quality; Real-time computing; Channel (broadcasting); End-to-end principle; Transmission (telecommunications); Coding (social sciences); Encoding (memory); Resilience (materials science); Error detection and correction; Scalable Video Coding; Distortion (music); Wireless; Computer network; Algorithm; Motion compensation; Artificial intelligence; Telecommunications; Bandwidth (computing); Metric (unit)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007510132,0.0005216306,0.0002970157,0.0002576913,0.0002567674,0.0005805749,0.000611806,0.0004137681,0.0007899319],"category_scores_gemma":[0.001268132,0.0001363527,0.0002161583,0.0002187946,0.0003180375,0.0006000832,0.0004953961,0.0005281236,0.0001467111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003082158,"about_ca_system_score_gemma":0.0003529211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00109914,"about_ca_topic_score_gemma":0.0011736,"domain_scores_codex":[0.9995946,0.00009097159,0.00002021035,0.00006260614,0.0001945521,0.00003694135],"domain_scores_gemma":[0.9996639,0.00009151079,0.00006947547,0.00003758006,0.000123989,0.00001360412],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000577635,0.0002963607,0.001815801,0.0002733479,0.0001186994,0.0003877773,0.0001629852,0.5267075,0.1931693,0.02259913,0.002968134,0.2509233],"study_design_scores_gemma":[0.00001890601,0.0002181289,0.0004913243,0.00001013608,0.00002859428,0.00009505953,0.00001219363,0.9767188,0.01995235,0.001410511,0.001031066,0.00001288602],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03790563,0.0007708116,0.959336,0.0001395116,0.00006694058,0.00006026672,0.00002249307,0.0002806317,0.001417641],"genre_scores_gemma":[0.8700298,0.0007253325,0.1266317,0.0001086464,0.00007754796,0.00006703305,0.00006074012,0.00003593736,0.002263325],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00109914,"threshold_uncertainty_score":0.003971815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04397811528196148,"score_gpt":0.3080058653917711,"score_spread":0.2640277501098096,"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."}}