{"id":"W2383916074","doi":"","title":"Data Receiving Quality based Adaptive Pull Push Scheduling Algorithm for P2P Streaming System","year":2011,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Peer-to-Peer Network Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Scalability; Scheduling (production processes); Push and pull; Real-time computing; Distributed computing; Push pull; Peer-to-peer; Data quality; Algorithm; Computer network; Database; Mathematical optimization","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008523532,0.0002976565,0.0003434715,0.0002490539,0.0004149972,0.0002306993,0.005148363,0.0001513056,0.00000172334],"category_scores_gemma":[0.000007841446,0.0003197147,0.00008927083,0.0009010435,0.00006422299,0.0005263067,0.002007941,0.0002258048,0.00007181363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001817121,"about_ca_system_score_gemma":0.0001241467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001031726,"about_ca_topic_score_gemma":0.00003921398,"domain_scores_codex":[0.9971974,0.00007398898,0.0005744783,0.001332567,0.0002333184,0.0005882762],"domain_scores_gemma":[0.9962086,0.0003135668,0.0002609639,0.002777484,0.0002862954,0.0001530685],"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":[0.000003442693,0.0001204483,0.00008047932,0.00004746456,0.00004535674,0.000001838609,0.0002522503,0.0002514479,0.0008307477,0.01832401,0.0013142,0.9787283],"study_design_scores_gemma":[0.0004314736,0.00006964703,0.001041324,0.0001334733,0.00002846854,0.00001803139,0.0001931867,0.9360749,0.005299247,0.001591252,0.05450483,0.0006141949],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003873994,0.0001045515,0.9949828,0.0004418403,0.00008304082,0.001726749,0.0002651157,0.001735042,0.0002734281],"genre_scores_gemma":[0.0337233,0.000001644186,0.964506,0.0002554434,0.0001846733,0.001156493,0.0001115667,0.00003101915,0.00002986862],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9781141,"threshold_uncertainty_score":0.9999255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1027306285246566,"score_gpt":0.3066224523509411,"score_spread":0.2038918238262845,"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."}}