{"id":"W3042805751","doi":"","title":"STRATEGIC IDLING AND DYNAMIC SCHEDULING IN OPEN-SHOP SERVICE NETWORK: CASE STUDY AND ANALYSIS","year":2016,"lang":"en","type":"article","venue":"International Conference on Information Systems","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Scheduling (production processes); Computer science; Operations research; Engineering; Operations management","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007519017,0.000137793,0.0002203688,0.0003224824,0.00009709341,0.001262344,0.0005208527,0.00005257006,0.00001204842],"category_scores_gemma":[0.00001588362,0.0001044587,0.00001929175,0.000469746,0.00001738385,0.00218784,0.0002222937,0.00009839673,0.00002368833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006324339,"about_ca_system_score_gemma":0.00007250821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003590484,"about_ca_topic_score_gemma":0.0007468213,"domain_scores_codex":[0.9986762,0.0001104711,0.0005218768,0.0002426535,0.0002771297,0.0001716989],"domain_scores_gemma":[0.9989749,0.0001478522,0.0002438257,0.0002236832,0.0003314828,0.00007828762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009206148,0.0001141031,0.02877061,0.00003301886,0.0005780088,0.0002082724,0.005001173,0.0839387,0.00002333623,0.6940771,0.00001438294,0.1871492],"study_design_scores_gemma":[0.001096466,0.00005481126,0.001624185,0.0001073357,0.00001750231,0.0001435112,0.004008445,0.9923779,2.342064e-7,0.0003692301,0.00005905766,0.0001412968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7675015,0.00003660893,0.2263791,0.001148997,0.0004392146,0.0005219357,0.000009199939,0.00006460794,0.00389882],"genre_scores_gemma":[0.9991757,0.00002612953,0.0004669903,0.0001738064,0.00003484627,0.00005709636,0.000006075544,0.000002857461,0.00005645434],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9084392,"threshold_uncertainty_score":0.9997745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0489791343714587,"score_gpt":0.3047264129459491,"score_spread":0.2557472785744904,"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."}}