{"id":"W4385332647","doi":"10.26599/tst.2023.9010013","title":"Fair $k$-Center Problem with Outliers on Massive Data","year":2023,"lang":"en","type":"article","venue":"Tsinghua Science & Technology","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Outlier; Cluster analysis; Computer science; Center (category theory); Big data; Artificial intelligence; Data mining; Theoretical computer science; Algorithm; Machine learning","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":[0.009118616,0.00135893,0.002969692,0.001403885,0.003185197,0.002873924,0.005114969,0.0030354,0.00284462],"category_scores_gemma":[0.03187134,0.0006232124,0.001513034,0.002590956,0.003202856,0.007456549,0.003908453,0.003191714,0.0004857217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003209284,"about_ca_system_score_gemma":0.003666921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006823773,"about_ca_topic_score_gemma":0.004042216,"domain_scores_codex":[0.9945868,0.001641374,0.0002242695,0.001500755,0.000956793,0.001089945],"domain_scores_gemma":[0.9803344,0.01265562,0.001529986,0.002349038,0.002053909,0.001077063],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001089795,0.0002244657,0.002352979,0.0001881592,0.0001283071,0.0002690698,0.0003837627,0.7778777,0.001651592,0.160234,0.008553167,0.04704699],"study_design_scores_gemma":[0.00005061504,0.0000443763,0.0002069327,0.000007061177,0.00001562149,0.00005721351,0.00009012519,0.8942547,0.0009990454,0.1035424,0.0007121036,0.00001985288],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05153985,0.0003706962,0.9441848,0.001344266,0.000127485,0.0001168954,0.0001848682,0.0005442586,0.001586918],"genre_scores_gemma":[0.7542613,0.00044521,0.239284,0.0004147922,0.0002657794,0.0002196157,0.0004299538,0.0002232657,0.004456053],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009118616,"threshold_uncertainty_score":0.04822445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04442015362285633,"score_gpt":0.27174426727092,"score_spread":0.2273241136480637,"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."}}