{"id":"W2061091622","doi":"10.1016/j.ejor.2009.07.007","title":"Heuristics for the Stochastic Eulerian Tour Problem","year":2009,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Heuristics; Eulerian path; Concatenation (mathematics); Mathematical optimization; Heuristic; Computer science; Grid; Probabilistic logic; Mathematics; Enhanced Data Rates for GSM Evolution; Algorithm; Combinatorics; Artificial intelligence; Applied mathematics; Lagrangian","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":[0.006451234,0.00008029381,0.0001044166,0.0001349662,0.0002764139,0.000198629,0.0003743991,0.00001560025,0.00007229518],"category_scores_gemma":[0.001378897,0.00005679106,0.00005703067,0.0002290362,0.00004484162,0.0001272161,0.00002212856,0.0004349903,0.00002312437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000608346,"about_ca_system_score_gemma":0.0001004382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.871845e-7,"about_ca_topic_score_gemma":2.245006e-7,"domain_scores_codex":[0.9982957,0.0004546101,0.0003938499,0.00007649896,0.000548692,0.0002306666],"domain_scores_gemma":[0.9980135,0.000757521,0.00004676314,0.000127934,0.0009577574,0.00009655298],"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":[0.00003154073,0.00001947176,0.000004828218,0.000007848531,0.00002502041,0.00001255427,0.0002325172,0.9635044,0.002267921,0.002689995,0.01478688,0.01641703],"study_design_scores_gemma":[0.001264057,0.0008207581,0.00581123,0.0001152713,0.00002291998,0.0001789431,0.0001833639,0.9570192,0.0005282714,0.001094517,0.03275062,0.000210826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002812512,0.0003460047,0.9886656,0.002574684,0.0002024654,0.0002537615,0.000006814874,0.00002442925,0.005113699],"genre_scores_gemma":[0.8327964,0.00004076276,0.1654343,0.0001165828,0.001014315,0.000002627592,0.000003092366,0.00003668655,0.0005551722],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8299839,"threshold_uncertainty_score":0.2315872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08847412350347472,"score_gpt":0.3704656567390892,"score_spread":0.2819915332356145,"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."}}