{"id":"W2077780773","doi":"10.14778/1920841.1920948","title":"Computing closed skycubes","year":2010,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Skyline; Linear subspace; Computer science; Representation (politics); Subspace topology; Computation; Theoretical computer science; Formal concept analysis; Closure (psychology); Space (punctuation); Algorithm; Data mining; Mathematics; 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":[0.0003694299,0.0001109788,0.0001176401,0.00006226274,0.0001296094,0.0001874777,0.002009249,0.00002631122,0.000009850418],"category_scores_gemma":[0.00004708142,0.00007450462,0.00007364334,0.0003017093,0.00005687314,0.0004755077,0.001300549,0.0001668201,0.00001747639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001139203,"about_ca_system_score_gemma":0.00001080773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001896379,"about_ca_topic_score_gemma":0.000001606943,"domain_scores_codex":[0.9989768,0.000002540256,0.0001991365,0.0002558594,0.0003379775,0.000227743],"domain_scores_gemma":[0.9994408,0.00002166441,0.0001621358,0.0002429743,0.00008789648,0.00004450471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00000417916,0.0001831956,0.006009809,0.0000882738,0.00005394026,8.248851e-7,0.0008306021,0.000005213068,0.1382371,0.7297162,0.01155299,0.1133176],"study_design_scores_gemma":[0.001812713,0.0001861595,0.03611552,0.0001661651,0.00006071124,0.00002826401,0.0003405831,0.06203452,0.755612,0.04870328,0.09416545,0.0007746831],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8837771,0.00006155071,0.01144852,0.008201713,0.003768556,0.00107434,0.000007004164,0.0004758353,0.0911854],"genre_scores_gemma":[0.9592908,0.000004814833,0.03988471,0.0002099865,0.000122836,0.000006366732,5.019893e-7,0.000006440837,0.0004735501],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.681013,"threshold_uncertainty_score":0.3733717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006308341498846236,"score_gpt":0.2082413297205273,"score_spread":0.2019329882216811,"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."}}