{"id":"W2768429747","doi":"10.4230/lipics.icalp.2018.29","title":"Interpolating between $k$-Median and $k$-Center: Approximation Algorithms for Ordered $k$-Median","year":2017,"lang":"en","type":"preprint","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Combinatorics; Mathematics; Approximation algorithm; Metric space; Center (category theory); Algorithm; Discrete 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","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001471937,0.0009453563,0.001073646,0.0008486206,0.001060922,0.002332333,0.001420647,0.0006397456,0.00006993093],"category_scores_gemma":[0.000625267,0.0009547881,0.0004877468,0.0001797269,0.0002333704,0.002787127,0.002147135,0.0007912461,0.0001618985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001902695,"about_ca_system_score_gemma":0.00008449041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005567459,"about_ca_topic_score_gemma":0.0005182694,"domain_scores_codex":[0.9953797,0.00001954119,0.002106768,0.0007606871,0.0006677682,0.001065463],"domain_scores_gemma":[0.9962252,0.00009664566,0.001464387,0.001323472,0.0007647029,0.0001255855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007412217,0.001536734,0.1077898,0.08135401,0.003353043,0.000007933252,0.01781998,0.001083485,0.00003686645,0.01725816,0.06664816,0.7023706],"study_design_scores_gemma":[0.006713656,0.0001007374,0.00394138,0.001516757,0.0005588535,0.000003697467,0.003214943,0.6415576,0.00003515085,0.01102728,0.3292058,0.002124064],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3123323,0.0002482217,0.5971289,0.01418072,0.01846744,0.02466022,0.008420133,0.001713247,0.02284891],"genre_scores_gemma":[0.9315547,0.0001510517,0.02838937,0.00235844,0.00473427,0.002003061,0.02971521,0.0002542204,0.0008396383],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7002465,"threshold_uncertainty_score":0.9992903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05952476036176745,"score_gpt":0.2948647382965404,"score_spread":0.235339977934773,"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."}}