{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003079886,0.0001447407,0.0001719026,0.000045451,0.0001149614,0.0001954336,0.000605214,0.00007547589,0.0007856308],"category_scores_gemma":[0.000002640294,0.0001103344,0.00009226905,0.0001726615,0.00002600043,0.0007868949,0.0001144103,0.0001458611,0.00008421943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004323468,"about_ca_system_score_gemma":0.00005234731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003303458,"about_ca_topic_score_gemma":0.000006042109,"domain_scores_codex":[0.9987196,0.00009440608,0.0002548902,0.0003316593,0.0002874116,0.0003120049],"domain_scores_gemma":[0.9992982,0.00006598624,0.00004162044,0.000425024,0.00004345756,0.0001257178],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000167303,0.0001789441,0.00002052157,0.000007966727,0.00001721344,0.000009527847,0.001329961,0.01400718,0.0006970917,0.01577246,0.01129644,0.956646],"study_design_scores_gemma":[0.0006977143,0.00003953273,0.0004021173,0.00001370713,0.000004791025,0.000005329416,0.00003086634,0.9657632,0.0005348498,0.00008663734,0.03222384,0.0001974859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002057854,0.00009187435,0.981312,0.003566479,0.0001076482,0.0001176636,1.926307e-7,0.0002388273,0.01250751],"genre_scores_gemma":[0.3251338,0.0001243669,0.6636897,0.005816066,0.0002588367,0.00001168321,0.000003354668,0.00001260377,0.004949547],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9564485,"threshold_uncertainty_score":0.8602107,"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."}}