{"id":"W2293751504","doi":"10.1007/978-3-319-26626-8_6","title":"A $$(5.83+\\epsilon )$$ ( 5.83 + ϵ ) -Approximation Algorithm for Universal Facility Location Problem with Linear Penalties","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Facility location problem; 1-center problem; Computer science; Mathematical optimization; Connection (principal bundle); Set (abstract data type); Total cost; Approximation algorithm; Algorithm; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001463369,0.00256498,0.002110071,0.001168943,0.001161999,0.002663992,0.00436769,0.002602221,0.06360694],"category_scores_gemma":[0.005615902,0.0008609626,0.002412165,0.002527056,0.0009976284,0.004221848,0.003439428,0.005664413,0.01848924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00299928,"about_ca_system_score_gemma":0.003550773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006465673,"about_ca_topic_score_gemma":0.01013349,"domain_scores_codex":[0.9984112,0.0002807206,0.00007898909,0.0003840137,0.0004779632,0.0003670695],"domain_scores_gemma":[0.9985662,0.0006766915,0.00006323154,0.0003887169,0.0001834363,0.000121707],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000500396,0.000777362,0.0005590112,0.000627469,0.0001093979,0.0001287615,0.0001342324,0.1154591,0.003159,0.1035551,0.1755293,0.5994608],"study_design_scores_gemma":[0.000331058,0.0002091576,0.0004971641,0.0001784669,0.00007652742,0.0003781284,0.0001003697,0.7981703,0.002923808,0.1538287,0.04325449,0.00005186165],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009676673,0.001208064,0.9158555,0.002068603,0.001061518,0.0003695789,0.001410141,0.00672095,0.06162899],"genre_scores_gemma":[0.05982853,0.000535558,0.8988224,0.000941536,0.0002933262,0.0006181673,0.002230724,0.001723191,0.03500663],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06360694,"threshold_uncertainty_score":0.2127864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03383172713465637,"score_gpt":0.2382939853279437,"score_spread":0.2044622581932874,"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."}}