{"id":"W2810740386","doi":"10.4230/lites-v005-i001-a001","title":"Risk-Aware Scheduling of Dual Criticality Job Systems Using Demand Distributions","year":2018,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Real-Time Systems Scheduling","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Criticality; Computer science; Exploit; Probabilistic logic; Scheduling (production processes); Job scheduler; Distributed computing; Mathematical optimization; Artificial intelligence; Mathematics; Computer network","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":[0.002597393,0.00104218,0.001221366,0.0005603041,0.0006193857,0.001502771,0.001474701,0.0008448191,0.001905389],"category_scores_gemma":[0.006955544,0.001024163,0.000782177,0.0004719376,0.001035934,0.001410642,0.001286052,0.001448351,0.0002320202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001428979,"about_ca_system_score_gemma":0.001845774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003892753,"about_ca_topic_score_gemma":0.002992964,"domain_scores_codex":[0.9988028,0.0003479816,0.00005308705,0.0002555372,0.0002406757,0.0002999019],"domain_scores_gemma":[0.9956979,0.002501279,0.0006409592,0.0002386057,0.0003098161,0.000611554],"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":[0.0001738698,0.00004141006,0.0008669423,0.00003007218,0.00002461391,0.00006626541,0.00004561528,0.9862585,0.001378918,0.007259713,0.0002773743,0.003576613],"study_design_scores_gemma":[0.000009123782,0.00002860637,0.0001375593,0.000002304494,0.000003662827,0.000008543327,0.0000102253,0.9951544,0.0002332471,0.004317696,0.00009059599,0.00000409732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3644508,0.0003979309,0.6286447,0.0008399524,0.0001107598,0.0001990271,0.0002923994,0.0005054952,0.004558966],"genre_scores_gemma":[0.9612471,0.00007926775,0.03694677,0.00007726208,0.00003712233,0.0000798503,0.0001461977,0.00008374348,0.001302515],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003892753,"threshold_uncertainty_score":0.01373649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0258004138254191,"score_gpt":0.2935407794287346,"score_spread":0.2677403656033155,"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."}}