{"id":"W4412931633","doi":"10.1007/s10951-025-00850-3","title":"Multiprocessor scheduling with testing: improved online algorithms and numerical experiments","year":2025,"lang":"en","type":"article","venue":"Journal of Scheduling","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Multiprocessing; Computer science; Parallel computing; Algorithm; Scheduling (production processes); Mathematical optimization; 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":[0.004803979,0.001487923,0.00127515,0.001015666,0.000696018,0.0009174672,0.002878581,0.00193157,0.003934686],"category_scores_gemma":[0.02635656,0.0006030676,0.0006291095,0.001728889,0.00129168,0.002988076,0.001334391,0.001866405,0.0003155834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001266233,"about_ca_system_score_gemma":0.001663865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00715664,"about_ca_topic_score_gemma":0.004588588,"domain_scores_codex":[0.9970382,0.001676707,0.0001710686,0.0002593804,0.000607341,0.00024731],"domain_scores_gemma":[0.9541284,0.03677429,0.001634612,0.004108698,0.002716142,0.0006379362],"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":[0.001592644,0.001758525,0.001855554,0.0001985391,0.0000521917,0.0000770292,0.00006605191,0.9273987,0.00338734,0.005244642,0.002183909,0.05618487],"study_design_scores_gemma":[0.00009941313,0.00009424704,0.00009902338,0.000003381927,0.000005658032,0.000006991069,0.000008013084,0.9975343,0.0008828337,0.001195133,0.00006690015,0.000004120811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.508771,0.0009740196,0.4713093,0.0008138359,0.0003670489,0.0004123482,0.0004671688,0.004056202,0.01282912],"genre_scores_gemma":[0.7818505,0.0001166323,0.216077,0.000112645,0.00005605656,0.0002428217,0.0002970405,0.0002356831,0.001011623],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00715664,"threshold_uncertainty_score":0.02540618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0197133417024572,"score_gpt":0.2697898431170733,"score_spread":0.2500765014146161,"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."}}