{"id":"W2096910555","doi":"10.1287/ijoc.2014.0600","title":"Districting for Arc Routing","year":2014,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"Deutsche Forschungsgemeinschaft","keywords":"Arc routing; Roulette; Tabu search; Social connectedness; Compact space; Mathematical optimization; Subroutine; Routing (electronic design automation); Computer science; Heuristic; Context (archaeology); Vehicle routing problem; Mathematics","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":[0.001920305,0.0001810923,0.0002252532,0.0001599262,0.0004369616,0.0002537524,0.0002237999,0.00007668334,0.000007979014],"category_scores_gemma":[0.001025416,0.0001618859,0.0001264492,0.0002229051,0.00001311125,0.0002141352,0.00003631431,0.0004723713,0.00001451922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001276681,"about_ca_system_score_gemma":0.00001701368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.15337e-7,"about_ca_topic_score_gemma":1.480022e-7,"domain_scores_codex":[0.9985714,0.00003412751,0.0006109102,0.0001111905,0.0002182353,0.0004541121],"domain_scores_gemma":[0.9986095,0.0007804119,0.0002108338,0.0001419631,0.0001192203,0.0001380954],"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":[0.000004545232,0.000004081634,0.0007231378,0.00002341625,0.00001844718,8.093079e-7,0.0002224312,0.7698478,0.0001234627,0.001318351,0.0001544187,0.2275592],"study_design_scores_gemma":[0.0005505147,0.00007320651,0.0005426023,0.0001385395,0.000008469851,0.00008043882,0.00007425296,0.9918287,0.0007855752,0.0003390765,0.005365251,0.0002133773],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1131932,0.00001243003,0.8779364,0.00005257423,0.000980597,0.00009654349,0.000001007097,0.0003363547,0.007390883],"genre_scores_gemma":[0.8107804,0.000003190934,0.188084,0.0001667829,0.0008964121,0.000001236178,0.00000252657,0.0000426745,0.0000227789],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6975872,"threshold_uncertainty_score":0.6601517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01790350182192475,"score_gpt":0.2714472516494821,"score_spread":0.2535437498275573,"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."}}