{"id":"W2002063544","doi":"10.1109/ieem.2007.4419320","title":"Stochastics in discrete logistics models: What can we do?","year":2007,"lang":"en","type":"article","venue":"","topic":"Mobile Agent-Based Network Management","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Norges Forskningsråd","keywords":"Computer science; Integer programming; Mathematical optimization; Presentation (obstetrics); Integer (computer science); Service (business); Network planning and design; Theoretical computer science; Algorithm; Mathematics; Programming language; Computer network","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":[0.0006583662,0.0001727106,0.0001581695,0.0001885984,0.00005219001,0.0003540004,0.0009465667,0.00005555582,0.00002237954],"category_scores_gemma":[0.00001577649,0.0001545293,0.00004200513,0.0005515302,0.00004815214,0.0005772546,0.0005523413,0.0001533113,0.00003072899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002038978,"about_ca_system_score_gemma":0.00004668527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001060655,"about_ca_topic_score_gemma":0.0009113486,"domain_scores_codex":[0.9982431,0.00003488237,0.0003387973,0.0004481896,0.0003820007,0.0005530609],"domain_scores_gemma":[0.9987968,0.0001540763,0.00007057899,0.0007927353,0.00004117886,0.0001446184],"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":[0.000005561313,0.00003786895,0.00004547942,0.00001241341,0.000008086577,0.0001205772,0.0003779312,0.3856543,0.000003361041,0.5019816,0.001127436,0.1106254],"study_design_scores_gemma":[0.0002883996,0.00006296466,0.00007341676,0.00005651708,0.000004969059,0.000002274585,0.0003370969,0.9594492,0.00005576145,0.03742485,0.002005709,0.0002388188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005186364,0.0003457476,0.9917809,0.00103112,0.0005469244,0.0003037343,8.829988e-7,0.0001409305,0.005331089],"genre_scores_gemma":[0.9132701,0.0003920798,0.08395712,0.001347812,0.00009562892,0.00001804001,0.000003505162,0.00001515758,0.000900524],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9127515,"threshold_uncertainty_score":0.6301523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0333365233209958,"score_gpt":0.2588748198957518,"score_spread":0.225538296574756,"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."}}