{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002819612,0.0002505101,0.0004018383,0.0002978241,0.00012321,0.000124365,0.0001993644,0.0001190259,0.000007606874],"category_scores_gemma":[0.0003440414,0.0002076178,0.00005991436,0.0006156451,0.00005383985,0.0003115982,0.00004033763,0.0005430522,0.000001322563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007366417,"about_ca_system_score_gemma":0.000111968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000427677,"about_ca_topic_score_gemma":4.628016e-7,"domain_scores_codex":[0.9986255,0.00002141121,0.0006172609,0.0001942481,0.0002458346,0.0002958114],"domain_scores_gemma":[0.9988983,0.0001610326,0.0001991268,0.0001370071,0.0004245661,0.0001799245],"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":[0.00008539126,0.0001768924,0.004180863,0.0001659399,0.0003116128,0.00004220348,0.0004432867,0.935854,0.01561143,0.00003225784,0.000008657175,0.04308752],"study_design_scores_gemma":[0.00173743,0.0001350358,0.0004810745,0.0005058724,0.00007296627,0.0001095939,0.001065769,0.9849932,0.01056025,0.0000370378,0.00006432173,0.0002374622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4045379,0.002953853,0.591511,0.0001614989,0.0004500788,0.00009970879,0.000002677441,0.0001353805,0.0001479811],"genre_scores_gemma":[0.420422,0.00005116245,0.5792235,0.00007650326,0.0001703693,0.000002760485,0.000001661548,0.00002820134,0.00002390481],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04913924,"threshold_uncertainty_score":0.8466407,"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."}}