{"id":"W2902521181","doi":"10.1109/tkde.2019.2923914","title":"Skyline Diagram: Efficient Space Partitioning for Skyline Queries","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"National Science Foundation","keywords":"Skyline; Computer science; Voronoi diagram; Set (abstract data type); Scalability; Data mining; Space (punctuation); Theoretical computer science; Database; 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":[],"consensus_categories":[],"category_scores_codex":[0.0002309428,0.0001570909,0.0001533866,0.0001472606,0.0001195592,0.0001817019,0.0005478185,0.00003693979,0.00001375769],"category_scores_gemma":[0.000007601591,0.0001533335,0.00003656233,0.0002575456,0.00001439796,0.0006841073,0.0000287176,0.0001113273,0.0000705338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001755021,"about_ca_system_score_gemma":0.00001622683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005432762,"about_ca_topic_score_gemma":0.00001112109,"domain_scores_codex":[0.9990132,0.000008840861,0.0001658721,0.0004643606,0.00009817605,0.0002495859],"domain_scores_gemma":[0.9989148,0.000121534,0.00002670463,0.0008236896,0.00003548211,0.00007779722],"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":[0.00006104619,0.001072651,0.00006924533,0.001110292,0.0003365323,0.00001396588,0.001336683,0.4267901,0.003780917,0.04125494,0.004738005,0.5194356],"study_design_scores_gemma":[0.0004411974,0.00007429068,0.00005250659,0.00006045029,0.0000199209,0.000002703075,0.0000158902,0.9479904,0.002570663,0.00002178977,0.04855021,0.0002000129],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002570845,0.0002964351,0.9949624,0.0002058437,0.001113345,0.0002833111,0.0001914386,0.0002241303,0.0001522801],"genre_scores_gemma":[0.9166816,0.00018358,0.08110204,0.00005191485,0.0001844808,0.0000837176,0.0002003703,0.00003373484,0.001478575],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9141107,"threshold_uncertainty_score":0.625276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0196882249210968,"score_gpt":0.2580137143152098,"score_spread":0.238325489394113,"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."}}