{"id":"W1559991472","doi":"10.1007/11407522_16","title":"LOMARC — Lookahead Matchmaking for Multi-resource Coscheduling","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Parallel computing; Scheduling (production processes); Workload; Schedule; Distributed computing; Context switch; Queue; Gang scheduling; Operating system; Computer network; Dynamic priority scheduling; Rate-monotonic scheduling; Mathematical optimization","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":[0.001240585,0.0006360359,0.0006374037,0.0009142938,0.0004674274,0.0007511951,0.003796034,0.0004182232,0.000009144127],"category_scores_gemma":[0.0001356251,0.0006352541,0.0002222069,0.0004959064,0.0004217939,0.0004844553,0.001320796,0.0007388713,0.0000235847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003565946,"about_ca_system_score_gemma":0.0004175655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007959113,"about_ca_topic_score_gemma":0.00002199486,"domain_scores_codex":[0.9958337,0.00003725067,0.0007134682,0.001778894,0.0007563463,0.0008803207],"domain_scores_gemma":[0.9971127,0.0005569511,0.0004455812,0.001359976,0.0003304931,0.0001942966],"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":[0.000006548455,0.00003913986,0.00001411904,0.00005000949,0.00001160669,0.00001825324,0.0005118766,0.3435626,0.00004461282,0.0169091,0.0000785167,0.6387536],"study_design_scores_gemma":[0.000382243,0.0001071939,0.00001141276,0.0004743705,0.000007215269,0.00004400266,1.091587e-7,0.965928,0.001138014,0.01988577,0.01129451,0.0007272016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000009881016,0.0006932477,0.9942932,0.0006817769,0.0007162349,0.0007073524,0.000005152202,0.0007632323,0.002129982],"genre_scores_gemma":[0.008905432,0.00003177059,0.9874932,0.002043552,0.0006369153,0.0000228861,0.000008890904,0.00005837962,0.0007989903],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6380264,"threshold_uncertainty_score":0.9996099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03323971504214483,"score_gpt":0.2921145699188136,"score_spread":0.2588748548766687,"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."}}