{"id":"W935408413","doi":"10.1007/978-3-662-47672-7_10","title":"Approximation Algorithms for Min-Sum k-Clustering and Balanced k-Median","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Cluster analysis; Computer science; Algorithm; Approximation algorithm; Combinatorics; Artificial intelligence; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007241601,0.0002644224,0.0002541277,0.0004943683,0.0001567561,0.0003390007,0.0004297317,0.0001176359,0.00003704276],"category_scores_gemma":[0.00009378229,0.0002526284,0.00004559522,0.0002137923,0.0001971565,0.0006004383,0.0004209685,0.0001512953,0.00003866345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009709344,"about_ca_system_score_gemma":0.00004763207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009901328,"about_ca_topic_score_gemma":0.0005613492,"domain_scores_codex":[0.9983208,0.000002492665,0.0003086943,0.0006461656,0.0004155124,0.0003063402],"domain_scores_gemma":[0.9992471,0.000034825,0.0001132982,0.0003223849,0.000255045,0.00002732616],"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":[0.00001542356,0.0000184643,0.00006258614,0.0005773525,0.00001131202,0.000003086884,0.0002066108,0.03165627,0.00002738494,0.007392908,0.0004681447,0.9595605],"study_design_scores_gemma":[0.0003014017,0.00001521788,0.0000970846,0.0001227566,0.00001369113,9.830654e-7,0.000001228819,0.9241474,0.000009557228,0.04693718,0.02803797,0.0003155388],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001019371,0.0001521264,0.9910334,0.001570004,0.001709235,0.0006645324,0.000004303704,0.00007798335,0.00468644],"genre_scores_gemma":[0.3711816,0.0001484728,0.5937527,0.01467728,0.01181783,0.000293828,0.0005622752,0.0002412673,0.007324742],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9592449,"threshold_uncertainty_score":0.9999926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04549324423460633,"score_gpt":0.2559878607425963,"score_spread":0.21049461650799,"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."}}