{"id":"W2060798327","doi":"10.1002/net.20408","title":"Big segment small segment global optimization algorithm on networks","year":2010,"lang":"en","type":"article","venue":"Networks","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Facility location problem; 1-center problem; Computer science; Cover (algebra); Mathematical optimization; Algorithm; Function (biology); Optimization problem; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004651164,0.0003062416,0.0002075428,0.0001045829,0.0002880047,0.0003106945,0.0003517176,0.0001841171,0.0009508543],"category_scores_gemma":[0.00002613688,0.0002968634,0.0001150105,0.0006581175,0.00004846832,0.000250488,0.000221906,0.0003367426,0.000306769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008043276,"about_ca_system_score_gemma":0.00001349879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005136367,"about_ca_topic_score_gemma":0.001092431,"domain_scores_codex":[0.998212,0.00001587701,0.0004085478,0.0005038641,0.000335139,0.0005245112],"domain_scores_gemma":[0.9991595,0.00001364011,0.0001156113,0.000514615,0.000152812,0.00004388428],"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":[0.00001975816,0.0001376165,0.0008835254,0.0000157123,0.00002831069,0.000002992911,0.000004184659,0.770019,9.74797e-7,0.004375021,0.01063281,0.2138801],"study_design_scores_gemma":[0.000467737,0.00001673183,0.002724447,0.00002140803,0.00004419589,5.442522e-7,0.000030851,0.9249285,0.000001152838,0.0001263978,0.07133523,0.000302763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005061885,0.00008169501,0.9510924,0.001524123,0.008667227,0.0007069599,0.000003615209,0.0004162526,0.03244582],"genre_scores_gemma":[0.9433749,0.0002434958,0.01761588,0.0199115,0.01520737,0.000263765,0.0008993128,0.0001035625,0.002380191],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9383131,"threshold_uncertainty_score":0.9999624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01670503028694023,"score_gpt":0.2133981429654627,"score_spread":0.1966931126785224,"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."}}