{"id":"W2139541175","doi":"10.1109/71.932715","title":"Efficient local search far DAG scheduling","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Parallel and Distributed Systems","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Computer science; Fair-share scheduling; Two-level scheduling; Dynamic priority scheduling; Rate-monotonic scheduling; Directed acyclic graph; Scheduling (production processes); Distributed computing; Incremental heuristic search; Algorithm; Schedule; Beam search; Search algorithm; Parallel computing; Mathematical optimization; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005995788,0.0003875254,0.0004696656,0.0002046341,0.0006578812,0.0005004492,0.0007080179,0.0002122121,0.0000112681],"category_scores_gemma":[0.000005445429,0.0003512555,0.0001662545,0.0008814215,0.0001291657,0.0001524048,0.00001257288,0.0004308931,0.0001998583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000133018,"about_ca_system_score_gemma":0.0001059408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003501562,"about_ca_topic_score_gemma":0.00001048669,"domain_scores_codex":[0.9969215,0.0002756758,0.000622135,0.0008067953,0.000626872,0.0007469928],"domain_scores_gemma":[0.9983481,0.0001927866,0.000122287,0.0007428001,0.000180687,0.0004133874],"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":[0.0000432969,0.0002253922,0.00006782845,0.00005182404,0.00006455224,0.00006864964,0.0002107433,0.9932756,0.00007379738,0.00199176,0.0001649262,0.003761603],"study_design_scores_gemma":[0.0009562667,0.0001595699,0.0001845328,0.0001227848,0.00001842675,0.000380029,0.0003611468,0.9927821,0.000009759519,0.00003476435,0.004580972,0.0004096329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02442358,0.0006260343,0.9716615,0.0002859877,0.001378586,0.000440723,0.0001567128,0.0005148768,0.0005119875],"genre_scores_gemma":[0.9981877,0.00005959961,0.001125536,0.00006729,0.00009476578,0.00005943097,0.00003477348,0.00002052646,0.0003503964],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9737641,"threshold_uncertainty_score":0.999894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02467852927785577,"score_gpt":0.2528306202013057,"score_spread":0.2281520909234499,"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."}}