{"id":"W1749728770","doi":"10.1109/tai.1999.809806","title":"A dynamic scheduling benchmark: design, implementation and performance evaluation","year":2003,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Testbed; Dynamic priority scheduling; Distributed computing; Scheduling (production processes); Fair-share scheduling; Two-level scheduling; Job shop scheduling; Schedule; Mathematical optimization; Computer network; Operating system","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":[],"consensus_categories":[],"category_scores_codex":[0.001116197,0.00007222442,0.00005560624,0.00006687109,0.000128221,0.0001162498,0.000153369,0.00002398152,0.000165919],"category_scores_gemma":[0.0000482385,0.00006698724,0.00001198758,0.000193322,0.00002358865,0.0006742728,0.00003716504,0.00004461673,0.00003344464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005430166,"about_ca_system_score_gemma":0.00008197782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001219943,"about_ca_topic_score_gemma":0.00002342049,"domain_scores_codex":[0.9990923,0.0001082825,0.0001715597,0.0002259062,0.0002393085,0.0001626783],"domain_scores_gemma":[0.9995558,0.00006699788,0.00004994498,0.0001821828,0.0001071734,0.00003793407],"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":[0.000002855281,0.00001858856,0.004743529,0.000005814084,0.000008514069,5.525999e-7,0.001099675,0.006314796,0.002477967,0.04817862,0.00003863463,0.9371104],"study_design_scores_gemma":[0.00006150383,0.00006843288,0.003014515,0.000005157103,0.000004517795,0.000006046188,0.0004486696,0.9591122,0.03095279,0.006173792,0.00005649934,0.00009586376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3587324,0.0000701194,0.6398031,0.00008230266,0.00008980835,0.0001788899,5.772185e-8,0.00003571285,0.001007555],"genre_scores_gemma":[0.736675,0.00002329899,0.2631766,0.00006810557,0.000003306742,0.00002115579,7.074908e-7,0.000002513823,0.00002924516],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9527974,"threshold_uncertainty_score":0.273166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05812196739568633,"score_gpt":0.3543296982474938,"score_spread":0.2962077308518075,"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."}}