{"id":"W1875314656","doi":"10.1109/icce.1995.517940","title":"Performance evaluation of MPEG-2 video coding for ATV","year":2005,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"High-definition television; Computer science; MPEG-2; Coding (social sciences); Bit rate; MPEG-4; Encoding (memory); Data compression; Peak signal-to-noise ratio; Transform coding; Flexibility (engineering); Signal-to-noise ratio (imaging); Video quality; Computer vision; Artificial intelligence; Computer hardware; Discrete cosine transform; Telecommunications; Image (mathematics); Engineering; Mathematics","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":[0.0006915643,0.00006007831,0.00008903322,0.00008499424,0.00007920257,0.00002791604,0.0005478167,0.00004016103,0.00001904699],"category_scores_gemma":[0.0001155778,0.00004808635,0.00003792428,0.0001566559,0.00002043113,0.00034173,0.0001326421,0.00003943636,0.00001221863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002973678,"about_ca_system_score_gemma":0.00004125991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000204528,"about_ca_topic_score_gemma":0.000001779388,"domain_scores_codex":[0.9992185,0.00001802033,0.0001663682,0.0001715302,0.0002968471,0.00012869],"domain_scores_gemma":[0.9992654,0.00007219324,0.00007475008,0.0003548899,0.0002160417,0.00001672271],"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":[0.000002675242,0.00001831468,0.0002655058,0.000009090546,0.000004044606,1.766385e-8,0.00008575594,0.001420296,0.00382868,0.02452817,0.001926717,0.9679107],"study_design_scores_gemma":[0.0002725648,0.00005244007,0.0007455279,0.00002598667,0.000004942201,0.000001096013,0.00002032951,0.8011416,0.1930484,0.001508441,0.003114922,0.0000636663],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3872522,0.0001599331,0.6017008,0.001868113,0.0001658518,0.0002507382,5.748477e-7,0.0004451301,0.008156566],"genre_scores_gemma":[0.9266739,0.00002007086,0.07289308,0.00006674454,0.0000244197,0.0000426378,3.664081e-7,0.000002502184,0.0002762259],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.967847,"threshold_uncertainty_score":0.1960904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07005893985143558,"score_gpt":0.3129217763537202,"score_spread":0.2428628365022846,"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."}}