{"id":"W2071422320","doi":"10.1109/icdsp.2009.5201166","title":"A new prediction structure for multiview video coding","year":2009,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Motion compensation; Multiview Video Coding; Coding (social sciences); Data compression; Reference frame; Artificial intelligence; Coding tree unit; Computer vision; Context-adaptive binary arithmetic coding; Video compression picture types; Exploit; Intra-frame; Random access; Algorithm; Frame (networking); Video processing; Video tracking; Decoding methods; Pixel; Mathematics; Computer network","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000292456,0.000478143,0.0004491441,0.0007477187,0.0003687874,0.0005299473,0.001036736,0.0005250688,0.002474145],"category_scores_gemma":[0.0006906511,0.0002148812,0.00035383,0.0007250886,0.00029203,0.0009423305,0.0008174408,0.0009315734,0.0009902319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005869256,"about_ca_system_score_gemma":0.0008411795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003441306,"about_ca_topic_score_gemma":0.003786684,"domain_scores_codex":[0.9997122,0.00002764902,0.00001971943,0.00005172128,0.000163602,0.0000251461],"domain_scores_gemma":[0.9997237,0.00003920092,0.00002595514,0.00005776489,0.0001353999,0.00001794807],"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":[0.0002193505,0.0000926413,0.0006496444,0.0001579906,0.00003490158,0.0001673025,0.0001373464,0.05629595,0.1419082,0.05519383,0.009216322,0.7359264],"study_design_scores_gemma":[0.0000419301,0.0002167617,0.0005842288,0.00004680831,0.00003061297,0.0004071731,0.00002634824,0.9032028,0.05148323,0.01340795,0.03049528,0.00005687568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004083514,0.0003783133,0.9936796,0.00007335407,0.00008388745,0.0000444295,0.0001166867,0.000462672,0.001077458],"genre_scores_gemma":[0.1151324,0.0006840113,0.87735,0.0002222865,0.0001653187,0.0002148232,0.0009228365,0.0001187591,0.005189458],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003441306,"threshold_uncertainty_score":0.00827688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02131429647811371,"score_gpt":0.2634075712530059,"score_spread":0.2420932747748922,"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."}}