{"id":"W1604003500","doi":"10.1016/j.ipl.2020.105959","title":"Approximation ratio of LD algorithm for multi-processor scheduling and the Coffman–Sethi conjecture","year":2020,"lang":"en","type":"preprint","venue":"Information Processing Letters","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; Wilfrid Laurier University","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada","keywords":"Counterexample; Job shop scheduling; Conjecture; Scheduling (production processes); Computer science; Parallel computing; Mathematics; Algorithm; Combinatorics; Discrete mathematics; Mathematical optimization; Embedded system","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004575511,0.0003211427,0.000424765,0.0001790799,0.0001897876,0.000414183,0.0002612502,0.0002766593,0.000003197637],"category_scores_gemma":[0.0002239847,0.0002649822,0.0001016774,0.0002175494,0.0001668046,0.0007300365,0.00008640194,0.0005823064,0.000003362017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005124407,"about_ca_system_score_gemma":0.00009381858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004823613,"about_ca_topic_score_gemma":4.473604e-7,"domain_scores_codex":[0.9984697,0.00003110829,0.0008135736,0.0001930245,0.0002884737,0.0002041128],"domain_scores_gemma":[0.9988329,0.000106083,0.0005266469,0.0001861223,0.000285323,0.00006290921],"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":[0.0000338053,0.000007408024,0.000003984599,0.004065338,0.00009676947,1.912729e-7,0.01078051,0.8881482,0.0001486374,0.0002018587,0.0002057635,0.09630755],"study_design_scores_gemma":[0.00202498,0.000008201861,0.000008661323,0.0003108641,0.00008635468,0.000004190645,0.0005523868,0.9941872,0.002112064,0.0002385569,0.0001782959,0.000288224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001524008,0.0005708517,0.9931901,0.002746932,0.0004470707,0.0009963202,0.00009250111,0.0003828382,0.0000494113],"genre_scores_gemma":[0.0861328,0.00007253256,0.9103984,0.002081609,0.0002094412,0.000354984,0.0006938012,0.00005096389,0.000005454572],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.106039,"threshold_uncertainty_score":0.9999802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02114070675591026,"score_gpt":0.2462968426351044,"score_spread":0.2251561358791941,"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."}}