{"id":"W2016713259","doi":"10.4018/jssci.2010040105","title":"A Least-Laxity-First Scheduling Algorithm of Variable Time Slice for Periodic Tasks","year":2010,"lang":"en","type":"article","venue":"International Journal of Software Science and Computational Intelligence","topic":"Cognitive Computing and Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nipissing University","funders":"","keywords":"Computer science; Scheduling (production processes); Algorithm; Task (project management); Dynamic priority scheduling; Earliest deadline first scheduling; Rate-monotonic scheduling; Mathematical optimization; Mathematics; Computer network; Quality of service","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007353444,0.0006769096,0.0007220478,0.001076343,0.0009490426,0.0006117955,0.0009944073,0.000406188,0.001974516],"category_scores_gemma":[0.001836657,0.0002876828,0.0004062341,0.0009256618,0.0004162996,0.000731166,0.0005318862,0.0006608766,0.000367199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007915413,"about_ca_system_score_gemma":0.002734643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004892168,"about_ca_topic_score_gemma":0.004553215,"domain_scores_codex":[0.9996653,0.0000663454,0.00003452694,0.00007321717,0.00009705547,0.00006361485],"domain_scores_gemma":[0.9993549,0.0001729439,0.00007372801,0.00008368304,0.0002442245,0.00007056542],"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":[0.001056809,0.0001926759,0.002352674,0.0003335833,0.00007033793,0.0002925188,0.0006188094,0.2239399,0.05696594,0.04052267,0.01059998,0.663054],"study_design_scores_gemma":[0.0001943267,0.0003274259,0.0006356166,0.00003339683,0.00003738499,0.0002986941,0.00009414949,0.9551948,0.01740648,0.0145151,0.01119905,0.00006350734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01566598,0.0002941671,0.981585,0.0000825731,0.00009976122,0.000108788,0.00005625801,0.001090903,0.001016539],"genre_scores_gemma":[0.2075603,0.0002056384,0.7900316,0.00007754235,0.00004976316,0.0002122385,0.0002526729,0.0001440586,0.001466144],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004892168,"threshold_uncertainty_score":0.009727418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01336327353218105,"score_gpt":0.2805500619703307,"score_spread":0.2671867884381496,"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."}}