{"id":"W1965263312","doi":"10.1145/1187335.1187338","title":"Perceptually optimized 3D transmission over wireless networks","year":2005,"lang":"en","type":"article","venue":"","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Bandwidth (computing); Packet loss; Perception; Quality (philosophy); Transmission (telecommunications); Wireless; Image quality; Network packet; Artificial intelligence; Computer network; Computer vision; Image (mathematics); Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009245789,0.0005489415,0.0004355953,0.0005548209,0.0004002704,0.001094538,0.0007522613,0.0005147794,0.001495538],"category_scores_gemma":[0.004014255,0.0003054072,0.0002316007,0.0006058294,0.0005433505,0.001405663,0.0008623345,0.0005452372,0.0003034044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005129793,"about_ca_system_score_gemma":0.0002861466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001106518,"about_ca_topic_score_gemma":0.00122244,"domain_scores_codex":[0.9992374,0.0002507522,0.00004931679,0.00007919496,0.0003097792,0.00007350017],"domain_scores_gemma":[0.9983159,0.0007676104,0.0002433717,0.0003148302,0.0003006088,0.00005766336],"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":[0.0008775077,0.0001444215,0.001799702,0.0004253344,0.00009798992,0.0004885731,0.0006012929,0.4704605,0.2104886,0.05665769,0.004308616,0.2536498],"study_design_scores_gemma":[0.00003791263,0.0001779185,0.001037496,0.000029029,0.00004997494,0.0003558396,0.000131514,0.9373182,0.03777266,0.01546152,0.007578767,0.00004919635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0564899,0.0009274172,0.9366342,0.000177272,0.00006011661,0.00006498333,0.00006743595,0.0006206832,0.004957921],"genre_scores_gemma":[0.7942205,0.001555993,0.2006748,0.00009879002,0.00007601811,0.00009098819,0.0001338321,0.0002014158,0.00294763],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001495538,"threshold_uncertainty_score":0.005003095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01596944512346202,"score_gpt":0.2826210955312016,"score_spread":0.2666516504077395,"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."}}