{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003151664,0.0002373405,0.0001636568,0.000247654,0.0001471236,0.0002577601,0.0002468099,0.0001980669,0.001817714],"category_scores_gemma":[0.0007823726,0.00008362505,0.00009724662,0.0002882894,0.0001540801,0.0003407325,0.0002091525,0.0003745058,0.0002690987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002879891,"about_ca_system_score_gemma":0.0004370831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001678965,"about_ca_topic_score_gemma":0.002825525,"domain_scores_codex":[0.9998344,0.00004077806,0.000006894527,0.00001588483,0.00008934285,0.00001258383],"domain_scores_gemma":[0.9998199,0.0000750847,0.00001645086,0.00002127095,0.000061289,0.000005967495],"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":[0.0003882505,0.00005255772,0.0005660859,0.0001852633,0.00002773274,0.0002769374,0.0001005561,0.09279381,0.1615775,0.0588588,0.01090074,0.6742718],"study_design_scores_gemma":[0.00004085411,0.0001208413,0.000543085,0.00003181193,0.000009830726,0.0001746204,0.00001702621,0.9424587,0.03078746,0.01274982,0.01304579,0.00002016401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02594143,0.00165432,0.9672707,0.0002331159,0.0001407519,0.0001018528,0.0001245928,0.0008532987,0.003679842],"genre_scores_gemma":[0.5660377,0.001685041,0.423136,0.0001665359,0.0001260135,0.0002187631,0.0004365292,0.00008564315,0.008107909],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001817714,"threshold_uncertainty_score":0.006080806,"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."}}