{"id":"W2070120025","doi":"10.1109/iccce.2010.5556849","title":"Fast mode decision for scalable video coding over wireless network","year":2010,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Federation for the Humanities and Social Sciences","keywords":"Computer science; Scalable Video Coding; Scalability; Bitstream; Real-time computing; Coding (social sciences); Video quality; Rate–distortion optimization; Coding tree unit; Motion compensation; Algorithm; Multiview Video Coding; Decoding methods; Computer hardware; Video processing; Video tracking","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002720951,0.0001411511,0.0001769734,0.00008031815,0.000321672,0.0002710966,0.001334469,0.0001481917,0.00003208057],"category_scores_gemma":[0.00009591747,0.0001090016,0.00008215327,0.0003153365,0.0000431132,0.0003946702,0.0005943375,0.0002290108,0.00002977479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000131249,"about_ca_system_score_gemma":0.00003199246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002390118,"about_ca_topic_score_gemma":0.00004808064,"domain_scores_codex":[0.9987228,0.00001093136,0.0002122458,0.000428485,0.0002224281,0.0004031189],"domain_scores_gemma":[0.998603,0.0003650617,0.00007031127,0.0008053795,0.00008218395,0.00007401065],"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":[0.00001342738,0.00003299462,0.0006974486,0.000008440215,0.000007083655,0.000001725635,0.00003798279,0.001296749,0.01131873,0.3165654,0.05306899,0.616951],"study_design_scores_gemma":[0.0003988516,0.00005150081,0.000493911,0.00006186921,0.000003124099,0.000006566838,0.0000141769,0.8447751,0.03089797,0.1102025,0.01285111,0.0002433192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06988939,0.00002674807,0.924793,0.0004972724,0.001113823,0.0001485551,0.000001158479,0.0009494933,0.002580537],"genre_scores_gemma":[0.756004,0.00001185111,0.2429064,0.0002335984,0.0001043402,0.00003864545,6.222896e-7,0.000008895209,0.000691598],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8434783,"threshold_uncertainty_score":0.4444958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01479742817915158,"score_gpt":0.2714763062313185,"score_spread":0.2566788780521669,"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."}}