{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006680935,0.000167381,0.0001417142,0.001933533,0.0004675347,0.0002024289,0.001757106,0.00006043219,0.0001126008],"category_scores_gemma":[0.000150923,0.0001342742,0.00001753287,0.006420731,0.0009156212,0.001229296,0.001189375,0.0001600344,0.003037504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006801219,"about_ca_system_score_gemma":0.00005864659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001214969,"about_ca_topic_score_gemma":0.0001590863,"domain_scores_codex":[0.9979452,0.000003369543,0.0002060746,0.0007750519,0.0004998828,0.0005704174],"domain_scores_gemma":[0.9985982,0.000005941647,0.00007701063,0.001156979,0.0001451175,0.00001679346],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001624156,0.00079626,0.0563838,0.000420166,0.00009884329,0.0001374436,0.0004556632,0.01183654,0.004544827,0.6434299,0.09741315,0.184321],"study_design_scores_gemma":[0.001212867,0.000126238,0.007128071,0.0001535272,0.00004724477,0.000004095437,0.004270168,0.09140862,0.0004599326,0.009351,0.8850314,0.0008068683],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7306995,0.00002021171,0.004316109,0.1038675,0.001728826,0.001626702,0.00002110165,0.005760964,0.1519591],"genre_scores_gemma":[0.996661,0.000006568929,0.0004878831,0.00138201,0.00007546311,0.00003305036,0.00005307385,0.00001580886,0.001285135],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7876182,"threshold_uncertainty_score":0.9977387,"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."}}