{"id":"W4283819012","doi":"10.1609/aaai.v36i4.20305","title":"Parameterized Approximation Algorithms for K-center Clustering and Variants","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; European Commission","keywords":"Combinatorics; Approximation algorithm; Center (category theory); Parameterized complexity; Complement (music); Upper and lower bounds; Mathematics; RADIUS; Dimension (graph theory); Generalization; Time complexity; Exponential time hypothesis; Euclidean geometry; Polynomial; VC dimension; Discrete mathematics; Mathematical analysis; Geometry; Crystallography; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002533652,0.002730965,0.002488307,0.001789251,0.001933982,0.00377636,0.007884499,0.003519229,0.00729003],"category_scores_gemma":[0.01424055,0.001081233,0.002474027,0.005793157,0.001871267,0.009241479,0.005251869,0.004708478,0.003587904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006373005,"about_ca_system_score_gemma":0.004126811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009797476,"about_ca_topic_score_gemma":0.01205038,"domain_scores_codex":[0.9952078,0.0008550658,0.0002968071,0.001755055,0.001218835,0.0006662518],"domain_scores_gemma":[0.9935098,0.002037588,0.0005622188,0.002610397,0.0009420342,0.0003379175],"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":[0.0009336987,0.0004972446,0.002019255,0.0008361866,0.0002468401,0.0001334427,0.0005445905,0.500939,0.004834659,0.1843947,0.04245776,0.2621627],"study_design_scores_gemma":[0.00009662197,0.00008282757,0.0002950213,0.00004902936,0.00005399658,0.0001768629,0.0001193876,0.8539165,0.002297461,0.1320258,0.01084299,0.00004356962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01754625,0.001999085,0.9664567,0.001165783,0.0002204259,0.0002251051,0.0007924349,0.004243182,0.007351039],"genre_scores_gemma":[0.2314408,0.001820447,0.7512255,0.0008512097,0.0003413212,0.0005499005,0.003809836,0.001480108,0.008480868],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009797476,"threshold_uncertainty_score":0.04623961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1178774763026291,"score_gpt":0.2868589415746862,"score_spread":0.1689814652720571,"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."}}