{"id":"W1987038930","doi":"10.1109/focs.2008.12","title":"Minimizing Movement in Mobile Facility Location Problems","year":2008,"lang":"en","type":"article","venue":"","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Facility location problem; Approximation algorithm; Computer science; 1-center problem; Mathematical optimization; Simple (philosophy); Linear programming relaxation; Metric (unit); Node (physics); Mathematics; Algorithm; Linear programming; Physics; Engineering","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":[0.001244826,0.001303049,0.001445334,0.0005820004,0.0008291842,0.001355957,0.001965516,0.001936639,0.004165714],"category_scores_gemma":[0.003879288,0.0006124049,0.001092068,0.001810302,0.001167823,0.003103497,0.001839516,0.001910027,0.0008066042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001818567,"about_ca_system_score_gemma":0.000968296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002388191,"about_ca_topic_score_gemma":0.002068959,"domain_scores_codex":[0.9985995,0.0005103092,0.00005682174,0.0003559902,0.0002361664,0.0002412715],"domain_scores_gemma":[0.9988958,0.0006574676,0.00015645,0.0001322471,0.00008962073,0.00006840428],"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":[0.0003582025,0.0001437237,0.0006121678,0.0004296037,0.00008572888,0.0001670314,0.0002181005,0.8181354,0.00215766,0.1023123,0.009674236,0.06570582],"study_design_scores_gemma":[0.0001036074,0.0001715255,0.0003979048,0.00005397926,0.0000385565,0.0002100234,0.0002025396,0.7962327,0.002219979,0.1881464,0.0121938,0.00002887107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04046611,0.0007063698,0.9496434,0.001136114,0.00008893363,0.0001611757,0.0004262979,0.0005784873,0.00679302],"genre_scores_gemma":[0.5155555,0.001159745,0.4693756,0.0004075778,0.0001849304,0.0005182719,0.001426773,0.0003197439,0.01105188],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004165714,"threshold_uncertainty_score":0.01393569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03677803264629648,"score_gpt":0.2222415991012249,"score_spread":0.1854635664549284,"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."}}