{"id":"W2978579878","doi":"10.1109/ism.workshops.2007.32","title":"A Prefetching Server for Reducing Startup Time of Embedded Multimedia","year":2007,"lang":"en","type":"article","venue":"Ninth IEEE International Symposium on Multimedia Workshops (ISMW 2007)","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Instruction prefetch; Cache; Web page; Static web page; World Wide Web; Web server; Operating system; Multimedia; Web API; Web development; The Internet","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001594861,0.0004300323,0.0004697863,0.0004138204,0.0001683708,0.0001659104,0.001649558,0.0002592668,0.0001377482],"category_scores_gemma":[0.0003778055,0.0004238843,0.0003773056,0.0003225425,0.0001071556,0.0006677627,0.0001992024,0.0004924068,0.0002077466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002921821,"about_ca_system_score_gemma":0.00009730859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001307615,"about_ca_topic_score_gemma":0.00002325131,"domain_scores_codex":[0.9962198,0.00009101536,0.0009853843,0.0009045178,0.00109001,0.0007093285],"domain_scores_gemma":[0.9962324,0.001657077,0.0005059327,0.0007669558,0.0005360271,0.0003016092],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002982639,0.002974732,0.002954316,0.0001763558,0.001394963,0.0001879942,0.01738989,0.06759261,0.5583299,0.002534476,0.04724334,0.2962388],"study_design_scores_gemma":[0.002903206,0.0002915932,0.001252017,0.0005343335,0.00004479305,0.00002522197,0.0001403532,0.9325274,0.05922749,0.0001400454,0.002215151,0.0006983873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4571944,0.0001365827,0.5206559,0.002277898,0.01253813,0.001341407,0.0001118482,0.0005751311,0.005168756],"genre_scores_gemma":[0.9201562,0.00002782202,0.07288928,0.0008826016,0.001542754,0.00007124164,0.0001038221,0.00007001466,0.004256205],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8649348,"threshold_uncertainty_score":0.9998213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02161653922834587,"score_gpt":0.2796815746052441,"score_spread":0.2580650353768982,"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."}}