{"id":"W2035478145","doi":"10.1016/j.jvcir.2005.12.002","title":"Special issue on emerging H.264/AVC video coding standard","year":2006,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Coding (social sciences); Computer science; Context-adaptive variable-length coding; Scalable Video Coding; Context-adaptive binary arithmetic coding; Data compression; Algorithm; Mathematics; Motion compensation; Statistics","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":[0.0005567762,0.0001034785,0.0001916023,0.0002747815,0.0002737803,0.0003456543,0.0006165728,0.00005003719,0.00002760501],"category_scores_gemma":[0.0002182206,0.0000898935,0.00006857466,0.0002939678,0.00007795187,0.0009413026,0.0002507952,0.0002748311,0.000008567847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004947469,"about_ca_system_score_gemma":0.00002958555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002096587,"about_ca_topic_score_gemma":0.000002683095,"domain_scores_codex":[0.9986517,0.0002105405,0.0004781306,0.000147666,0.0003884124,0.0001235842],"domain_scores_gemma":[0.9984922,0.0002356879,0.0005138176,0.0004284825,0.000288396,0.00004139352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002047877,0.0003086689,0.001412299,0.00003031894,0.00004164652,0.0000244442,0.001271591,0.0003090152,0.02991418,0.03404943,0.127783,0.8046506],"study_design_scores_gemma":[0.007029281,0.003106883,0.03426836,0.001483874,0.0001004573,0.0004794585,0.006599327,0.06820783,0.437963,0.07750749,0.3619435,0.001310538],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1893655,0.001654344,0.7240834,0.02757048,0.001724576,0.0004762734,0.000004009154,0.0004643928,0.05465701],"genre_scores_gemma":[0.9630036,0.001127204,0.03424842,0.00013711,0.001190817,0.000003979834,0.000003220453,0.000009798863,0.0002758017],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8033401,"threshold_uncertainty_score":0.3665751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02392515684518569,"score_gpt":0.355031431573626,"score_spread":0.3311062747284403,"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."}}