{"id":"W2885204832","doi":"10.1007/s10707-017-0293-2","title":"Uncertain Voronoi cell computation based on space decomposition","year":2017,"lang":"en","type":"article","venue":"GeoInformatica","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"German Academic Exchange Service London; Bundesministerium für Bildung und Forschung; Deutsche Forschungsgemeinschaft; Alberta Innovates - Technology Futures; Research Grants Council, University Grants Committee; Deutscher Akademischer Austauschdienst; National Science Foundation","keywords":"Voronoi diagram; Centroidal Voronoi tessellation; k-nearest neighbors algorithm; Computation; Computer science; Space partitioning; Computational geometry; Object (grammar); Nearest neighbor search; Mathematics; Algorithm; Data mining; Artificial intelligence; Geometry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000262531,0.0001134799,0.0001000883,0.00009363204,0.0004801681,0.001026,0.0009953358,0.00002840693,0.00001985251],"category_scores_gemma":[0.00001610944,0.0001030844,0.00004246576,0.00005998287,0.00003136279,0.002003254,0.0002446561,0.00005593591,0.0008258958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003250075,"about_ca_system_score_gemma":0.00002513151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002268194,"about_ca_topic_score_gemma":0.000002400366,"domain_scores_codex":[0.9991527,0.00001776139,0.0001836554,0.0001513585,0.0002740625,0.0002204651],"domain_scores_gemma":[0.9988253,0.00004668115,0.0002006226,0.0008251223,0.00003458795,0.00006770538],"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":[0.00004917109,0.0004655784,0.0003551485,0.0004436428,0.00003699804,0.00005408038,0.00175026,0.06332055,0.00008909177,0.1446344,0.07394648,0.7148545],"study_design_scores_gemma":[0.0004656911,0.00008442519,0.00181802,0.00002735174,0.000004495153,7.28268e-7,0.00002089116,0.9903045,0.0003508485,0.000963106,0.005819623,0.0001403561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001127407,0.000001936923,0.8838391,0.002210282,0.000274628,0.0001706979,0.000005057191,0.0001165189,0.1122544],"genre_scores_gemma":[0.7713549,0.000002381356,0.2269544,0.0009784678,0.00005414681,0.00001276333,0.00008321065,0.000006334757,0.0005534545],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9269839,"threshold_uncertainty_score":0.9999521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01413476570654418,"score_gpt":0.2716939366836894,"score_spread":0.2575591709771452,"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."}}