{"id":"W4390738300","doi":"10.1016/j.asoc.2024.111247","title":"A multi-objective fitness dependent optimizer for workflow scheduling","year":2024,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Brandon University","funders":"","keywords":"Computer science; Scheduling (production processes); Workflow; Mathematical optimization; Distributed computing; Database; Mathematics","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":[0.001440148,0.0009394179,0.001255131,0.0006068141,0.00047019,0.0008598583,0.001316117,0.001302107,0.003184498],"category_scores_gemma":[0.001773913,0.0005563638,0.0009098688,0.0006288919,0.0003864351,0.0005544004,0.000888703,0.001394944,0.0007564246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005637847,"about_ca_system_score_gemma":0.001097894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003993145,"about_ca_topic_score_gemma":0.004589718,"domain_scores_codex":[0.9995296,0.0001798259,0.00002437535,0.00007290069,0.0001434191,0.00004978853],"domain_scores_gemma":[0.9995455,0.0002206713,0.00004373116,0.00004813347,0.0001036283,0.00003820788],"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":[0.0001377587,0.0001161718,0.0003116885,0.00006044374,0.00008204926,0.00006232384,0.00002239287,0.910151,0.005053802,0.002713556,0.001378504,0.07991029],"study_design_scores_gemma":[0.00001426213,0.00002950847,0.00005785247,0.000001965196,0.000006333677,0.000005228063,0.00000163501,0.9989961,0.0003459546,0.0003139239,0.0002243883,0.00000278113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02902186,0.0003079619,0.9646839,0.0001951761,0.0001257322,0.00010821,0.0001012174,0.001720878,0.003735094],"genre_scores_gemma":[0.3763365,0.0001977235,0.614148,0.0002363905,0.0001021942,0.0002569661,0.0003099171,0.0005242088,0.007888004],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003993145,"threshold_uncertainty_score":0.0106532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02324533769168598,"score_gpt":0.2740252233984439,"score_spread":0.2507798857067579,"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."}}