{"id":"W2006707333","doi":"10.1016/s0925-7721(02)00102-5","title":"Fast approximations for sums of distances, clustering and the Fermat–Weber problem","year":2002,"lang":"en","type":"article","venue":"Computational Geometry","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":90,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Fermat's Last Theorem; Mathematics; Cluster analysis; Dimension (graph theory); Combinatorics; Randomized algorithm; Euclidean geometry; Preprocessor; Constant (computer programming); Set (abstract data type); Point (geometry); Approximation algorithm; Discrete mathematics; Algorithm; Computer science; Geometry; Statistics; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0002240979,0.00007222439,0.0001083682,0.0001022143,0.0001398752,0.0001490404,0.0003742343,0.00001518797,0.00001096382],"category_scores_gemma":[0.00002150118,0.00005333398,0.00003810597,0.0003843162,0.00009836787,0.0004195885,0.0002266164,0.00004041208,0.000006065902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008239372,"about_ca_system_score_gemma":0.000005163811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002250795,"about_ca_topic_score_gemma":0.000001208733,"domain_scores_codex":[0.9993129,0.00001959818,0.0001916952,0.0001736905,0.0001867558,0.0001153967],"domain_scores_gemma":[0.999392,0.0002556007,0.00009318488,0.0001627989,0.00007106513,0.00002531254],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001143167,0.00008566021,0.0002952054,0.0002874528,0.00007685941,9.637745e-7,0.000874025,0.03023381,0.000005990593,0.7811024,0.005988257,0.1810379],"study_design_scores_gemma":[0.0007646283,0.00001312432,0.001104758,0.00001491789,0.000006064617,0.000003507247,0.00003164266,0.9682326,0.000005106459,0.02600032,0.003750272,0.00007310922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001130787,0.0002846087,0.995032,0.001336304,0.00009521665,0.0003027701,0.00003281012,0.00003550233,0.001749998],"genre_scores_gemma":[0.519449,0.00002255314,0.4789459,0.0002419684,0.00006493003,0.0000700042,0.00005956727,0.000007748262,0.001138317],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9379987,"threshold_uncertainty_score":0.2174897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01932622633743269,"score_gpt":0.2275792836494696,"score_spread":0.2082530573120369,"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."}}