{"id":"W1973201650","doi":"10.1007/s00453-012-9690-y","title":"A Distributed O(1)-Approximation Algorithm for the Uniform Facility Location Problem","year":2012,"lang":"en","type":"article","venue":"Algorithmica","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Theory of computation; Randomized algorithm; Facility location problem; Metric (unit); Bounded function; Metric space; Constant (computer programming); Approximation algorithm; Deterministic algorithm; Set (abstract data type); Connection (principal bundle); Mathematics; Computer science; Binary logarithm; Distributed algorithm; Pairwise comparison; Algorithm; Point (geometry); Exponent; Discrete mathematics; Mathematical optimization; Distributed computing","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":[0.002223722,0.001502042,0.002330639,0.001148969,0.001607029,0.002537651,0.005693488,0.002919186,0.01368047],"category_scores_gemma":[0.00863507,0.0007997794,0.001316249,0.002680221,0.001481765,0.004283135,0.004266891,0.003001048,0.002400822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003002096,"about_ca_system_score_gemma":0.004747319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006731436,"about_ca_topic_score_gemma":0.009711984,"domain_scores_codex":[0.9977583,0.0005442757,0.0001044894,0.0006414414,0.0005320344,0.0004194946],"domain_scores_gemma":[0.9962745,0.002041616,0.0002067191,0.0007742275,0.0004037763,0.0002991436],"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":[0.001832485,0.0009554434,0.001647147,0.0004257818,0.000141454,0.0001714295,0.0002454187,0.6016734,0.004914811,0.06368072,0.03460762,0.2897043],"study_design_scores_gemma":[0.0003484602,0.00007520657,0.0002095818,0.00001351711,0.00002906531,0.00006457954,0.00004068209,0.964328,0.0006416834,0.03231123,0.001923822,0.00001415106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02747734,0.0005804083,0.9579009,0.001363493,0.0003374818,0.0002459836,0.000333975,0.001746159,0.01001424],"genre_scores_gemma":[0.2378985,0.0003189851,0.7512204,0.0005053977,0.0002563692,0.0005874088,0.0009155446,0.0003621489,0.007935224],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01368047,"threshold_uncertainty_score":0.04576582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0256849500484895,"score_gpt":0.238173884461227,"score_spread":0.2124889344127375,"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."}}