{"id":"W1512940807","doi":"10.1007/978-3-540-85958-1_7","title":"An Application of Constraint Programming to Superblock Instruction Scheduling","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Compiler; Parallel computing; Heuristics; Scheduling (production processes); Instruction scheduling; Suite; Optimizing compiler; Programming language; Mathematical optimization; Two-level scheduling; Schedule; Dynamic priority scheduling; Operating system; 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"],"consensus_categories":[],"category_scores_codex":[0.0004034863,0.0003093568,0.0003437241,0.0008846586,0.0002047235,0.0001920499,0.001194164,0.0002261963,0.000008715243],"category_scores_gemma":[0.00005030031,0.0003243778,0.00007588662,0.0007418701,0.0005244515,0.0006228742,0.0002637703,0.000381418,0.00001068214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002102749,"about_ca_system_score_gemma":0.0004615936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003210394,"about_ca_topic_score_gemma":0.00007941252,"domain_scores_codex":[0.997385,0.00002449857,0.000542505,0.001051277,0.0006555813,0.0003411045],"domain_scores_gemma":[0.9981759,0.00009105403,0.0002670198,0.0008957506,0.0003796447,0.0001906794],"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":[0.00000263023,0.00001544853,0.00015882,0.00001286593,0.000003239973,0.000003732184,0.0006188162,0.1786065,0.0004843167,0.006610901,4.646917e-7,0.8134823],"study_design_scores_gemma":[0.0002708174,0.0002197862,0.0004824302,0.0001685874,0.000005361969,0.0001817626,0.000001206165,0.9898078,0.002895905,0.004787887,0.0006791056,0.0004993406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001364604,0.00004005876,0.9965771,0.0002661189,0.0006113123,0.0005879201,0.000002904892,0.0001673008,0.0003827128],"genre_scores_gemma":[0.4095517,0.00001406595,0.5901234,0.0001756016,0.0001028107,0.000009038398,0.000005435328,0.00001179845,0.000006194585],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8129829,"threshold_uncertainty_score":0.9999208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01290976998663026,"score_gpt":0.2464877991268181,"score_spread":0.2335780291401878,"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."}}