{"id":"W1832526807","doi":"10.1007/11557654_29","title":"Convergence of the Discrete FGDLS Algorithm","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Real-Time Systems Scheduling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Monotonic function; Computer science; Convergence (economics); Workload; Algorithm; Mathematical optimization; Variable (mathematics); Scheduling (production processes); 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":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.001265362,0.0005609898,0.0006808178,0.0004481898,0.0002457266,0.0003526924,0.007484824,0.000328107,0.00003273442],"category_scores_gemma":[0.0001025089,0.0004023002,0.000274201,0.0008978819,0.001125284,0.0008184863,0.002559134,0.000847334,0.00005949265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002668182,"about_ca_system_score_gemma":0.0007843324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000639131,"about_ca_topic_score_gemma":0.00004278737,"domain_scores_codex":[0.9951859,0.00007847932,0.0008831095,0.00150018,0.001646692,0.0007057011],"domain_scores_gemma":[0.9956484,0.0004246061,0.0007056672,0.002731532,0.000324142,0.0001655843],"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":[0.000002049391,0.00002067979,0.0001796229,0.00006627916,0.00002290456,0.00003089135,0.001046539,0.03983103,0.0004345967,0.02348496,0.00002516175,0.9348553],"study_design_scores_gemma":[0.0002558572,0.0001020569,0.000539466,0.001012447,0.00001270516,0.0001237975,3.728975e-7,0.9429709,0.008331734,0.04358448,0.002245203,0.0008209688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00007039127,0.0006684501,0.989992,0.0007605245,0.003255733,0.0005288388,0.000007434164,0.00009249736,0.004624108],"genre_scores_gemma":[0.1453341,0.00005806321,0.851519,0.0006550348,0.0008457894,0.00001116671,0.000001602497,0.0000495225,0.001525747],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9340343,"threshold_uncertainty_score":0.9998429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01228171881359969,"score_gpt":0.2377750165834636,"score_spread":0.2254932977698639,"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."}}