{"id":"W2071226715","doi":"10.1007/s10951-006-8790-4","title":"Approximation algorithms for shop scheduling problems with minsum objective: A correction","year":2006,"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 British Columbia","funders":"","keywords":"Computer science; Scheduling (production processes); Mathematical optimization; Approximation algorithm; Job shop scheduling; Algorithm; Flow shop scheduling; Mathematics; Schedule","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":[0.0005964482,0.0002412928,0.0003474485,0.0003523947,0.0001589826,0.0001477049,0.0001493499,0.0001456732,0.000009682778],"category_scores_gemma":[0.00009875523,0.0002104228,0.0001439905,0.0004545481,0.00003558646,0.0005493861,0.000009600904,0.0003838671,0.000003862619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000177108,"about_ca_system_score_gemma":0.00008182069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006971667,"about_ca_topic_score_gemma":0.000006557671,"domain_scores_codex":[0.9984262,0.00002467931,0.0006900985,0.0001799851,0.0003616207,0.0003174138],"domain_scores_gemma":[0.998678,0.00012731,0.0003517144,0.0001254055,0.0006201681,0.00009745121],"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":[0.00005361565,0.00006063578,0.0004673084,0.00009272301,0.00007762675,0.000003840669,0.0002094793,0.9899993,0.002050718,0.00007121559,0.00005250071,0.006861053],"study_design_scores_gemma":[0.001515928,0.0001952899,0.0002141484,0.0003345863,0.00008796361,0.0001935917,0.0006650843,0.9870935,0.008976422,0.0002776666,0.0001670957,0.0002787346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1360596,0.0007611378,0.8609313,0.00007064141,0.001385839,0.0002815639,0.00000324424,0.0001600803,0.0003466316],"genre_scores_gemma":[0.3778686,0.0000362603,0.6210184,0.00001301339,0.0008867722,0.0000222818,0.00001298024,0.00006094453,0.00008076875],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.241809,"threshold_uncertainty_score":0.8580793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01281881357312941,"score_gpt":0.230134956859749,"score_spread":0.2173161432866196,"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."}}