{"id":"W4247636315","doi":"10.1080/07408170108936860","title":"Location of facilities on a network with groups of demand points","year":2001,"lang":"en","type":"article","venue":"IIE Transactions","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tabu search; Simulated annealing; Metaheuristic; Mathematical optimization; Facility location problem; Set (abstract data type); Computer science; Operations research; 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":[],"consensus_categories":[],"category_scores_codex":[0.0001459537,0.0000960385,0.000131695,0.0001358441,0.00009506528,0.00001539052,0.00008656402,0.00002800181,0.0007158545],"category_scores_gemma":[0.000008738502,0.00008491048,0.00004265888,0.0006884328,0.0000622029,0.0003388526,0.000003879698,0.00005282106,0.00006645436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001277787,"about_ca_system_score_gemma":0.00001393979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001059993,"about_ca_topic_score_gemma":0.002061861,"domain_scores_codex":[0.9992937,0.000007715224,0.0002464628,0.0001360915,0.0001830274,0.0001330305],"domain_scores_gemma":[0.9995269,0.00001414714,0.00006313794,0.0001905723,0.000197358,0.000007885696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0009948852,0.001070407,0.01066577,0.002237557,0.0003069196,0.000003010085,0.0008500977,0.9035186,0.0001591321,0.06460121,0.002288292,0.01330409],"study_design_scores_gemma":[0.01021147,0.0009409284,0.4511261,0.002416206,0.001588481,0.00001222486,0.01721861,0.1568078,0.001650165,0.02507698,0.3302315,0.002719561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5481223,0.0000996429,0.4267823,0.002049741,0.0003624015,0.0004751623,0.000006303355,0.0001168479,0.02198522],"genre_scores_gemma":[0.998498,0.00003216827,0.0001430891,0.0001628136,0.00006742816,0.00002201453,0.00001038879,0.000007486951,0.001056671],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7467108,"threshold_uncertainty_score":0.7838104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02142236927326423,"score_gpt":0.2122296372379164,"score_spread":0.1908072679646521,"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."}}