{"id":"W2079632749","doi":"10.1016/s0167-8655(01)00032-0","title":"Improving image and video transmission quality over ATM with foveal prioritization and priority dithering","year":2001,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Asynchronous Transfer Mode; Dither; Computer network; Foveal; ATM adaptation layer; Network packet; Asynchronous communication; FIFO (computing and electronics); Transmission (telecommunications); Image quality; Multicast; Multimedia; Real-time computing; Image (mathematics); Artificial intelligence; Telecommunications; Computer hardware; Bandwidth (computing)","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":[0.0002573497,0.0001586719,0.0001528772,0.0000549348,0.0001629681,0.0002446654,0.0001044672,0.00004994631,0.00001942096],"category_scores_gemma":[0.000009965712,0.0001426976,0.00002656698,0.000124812,0.00005743836,0.0007626362,0.00003933481,0.0001414347,0.000004909903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002592429,"about_ca_system_score_gemma":0.00001373588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007908217,"about_ca_topic_score_gemma":0.00002634714,"domain_scores_codex":[0.9988163,0.0001110913,0.0002136804,0.0004276749,0.0002014621,0.0002298036],"domain_scores_gemma":[0.9994664,0.0001011537,0.00011314,0.00015398,0.0000504868,0.0001149051],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00002946938,0.00001789592,0.01319827,0.00003643369,0.00001084337,0.00001823194,0.0002625086,0.000006336562,0.007016177,0.00001138662,0.00002401277,0.9793684],"study_design_scores_gemma":[0.009160329,0.0003997535,0.6088964,0.0006268092,0.0001220484,0.000372351,0.0001305498,0.3742318,0.001717375,0.0005996233,0.002007674,0.00173528],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4716552,0.00004098113,0.5263289,0.001669154,0.0000499472,0.000134593,0.000002189256,0.00009066729,0.00002842749],"genre_scores_gemma":[0.9875505,0.00008047744,0.009796347,0.002376428,0.0001406557,0.00002217837,0.00001017971,0.00001352139,0.000009695239],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9776332,"threshold_uncertainty_score":0.5819038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01132996244542983,"score_gpt":0.2266670444165305,"score_spread":0.2153370819711006,"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."}}