{"id":"W4232826557","doi":"10.1109/ismw.2007.4475961","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":"Distributed and Parallel Computing Systems","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; Web API; Operating system; Multimedia; 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":[0.001996433,0.0004613627,0.0005441364,0.0003439683,0.0001709503,0.0001991636,0.001967985,0.0002935309,0.0001139983],"category_scores_gemma":[0.000349439,0.0004512488,0.0003383964,0.0004286965,0.0001019221,0.0005038357,0.0002001279,0.0004323384,0.00024959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002711399,"about_ca_system_score_gemma":0.0001145252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007732795,"about_ca_topic_score_gemma":0.00001112437,"domain_scores_codex":[0.9958314,0.0001183839,0.001200765,0.0009364551,0.001116278,0.000796759],"domain_scores_gemma":[0.9957964,0.001809436,0.0006835964,0.0007948771,0.0005907062,0.0003249432],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002800832,0.004356486,0.005048521,0.0004872917,0.002182916,0.0002437559,0.03420958,0.4204691,0.1688665,0.005535331,0.1321469,0.2236528],"study_design_scores_gemma":[0.002610029,0.0002717364,0.00138882,0.0006913744,0.00003039905,0.00002867622,0.00009652387,0.9600438,0.02605362,0.0001433439,0.007951385,0.0006902818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.127288,0.00007890397,0.853178,0.001078818,0.01103503,0.001170658,0.0001346399,0.0004603999,0.005575544],"genre_scores_gemma":[0.884474,0.000009338706,0.1103607,0.0003863369,0.001780819,0.00005550702,0.0002011588,0.00006016477,0.002671924],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.757186,"threshold_uncertainty_score":0.9997939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02115200372349308,"score_gpt":0.2882540976143869,"score_spread":0.2671020938908938,"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."}}