{"id":"W2142213242","doi":"10.1109/ccece.2002.1013069","title":"Hierarchical adaptive control protocol for video streaming","year":2002,"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":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Video streaming; Network packet; Real Time Streaming Protocol; Merge (version control); Computer network; Dropout (neural networks); Protocol (science); Real-time computing; Scheme (mathematics); Adaptive control; Adaptation (eye); Live streaming; Control (management); The Internet; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001031701,0.000112309,0.0001387432,0.00008137849,0.000154808,0.0001060228,0.0008147854,0.00005653964,0.00005198988],"category_scores_gemma":[0.0001016778,0.00008098822,0.00007120177,0.0001510246,0.00004569007,0.0002176546,0.0001642799,0.0001214405,0.00003637515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001707739,"about_ca_system_score_gemma":0.00001147566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000371947,"about_ca_topic_score_gemma":0.000001153335,"domain_scores_codex":[0.9990568,0.0000296759,0.0001688188,0.0003232234,0.000156901,0.0002646004],"domain_scores_gemma":[0.9991514,0.000249996,0.00005650367,0.0004301564,0.00005784752,0.00005408657],"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":[0.00003834123,0.0001610865,0.00009935354,0.00001702428,0.00002026158,0.000007923496,0.0001162186,0.00006766879,0.0006012559,0.3822003,0.03467347,0.5819971],"study_design_scores_gemma":[0.002633683,0.0005685723,0.00008703135,0.00005509011,0.000002727472,0.00001060817,0.00003456585,0.8888971,0.008550959,0.04189182,0.05701702,0.0002508435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"protocol","genre_scores_codex":[0.00003861351,0.000005077127,0.9511323,0.001940618,0.00004115177,0.03991123,0.000001221475,0.0009899335,0.005939844],"genre_scores_gemma":[0.3861798,3.070269e-7,0.1854271,0.0008227655,0.00005939112,0.4257565,1.057407e-7,0.00001038797,0.001743648],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8888294,"threshold_uncertainty_score":0.3302604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05609023574440535,"score_gpt":0.2916807805551515,"score_spread":0.2355905448107461,"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."}}