{"id":"W2111811740","doi":"10.14778/2021017.2021018","title":"On pruning for top-k ranking in uncertain databases","year":2011,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of Alberta","funders":"","keywords":"Tuple; Pruning; Ranking (information retrieval); Rank (graph theory); Computer science; Parameterized complexity; Key (lock); Range (aeronautics); Learning to rank; Semantics (computer science); Function (biology); Computation; Ranking SVM; Task (project management); Database; Information retrieval; Artificial intelligence; Data mining; Mathematics; Algorithm; Programming language; Combinatorics; Discrete 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":[0.0004612069,0.000101547,0.0001164253,0.0001121695,0.00006827272,0.00005132109,0.001164168,0.00001247392,0.000006410129],"category_scores_gemma":[0.00008515493,0.00007079518,0.00004917399,0.0002733176,0.00002409624,0.0005428378,0.0005742624,0.00006291013,0.000002545874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003373865,"about_ca_system_score_gemma":0.000009701494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008427108,"about_ca_topic_score_gemma":0.000003715605,"domain_scores_codex":[0.9991013,0.000003690255,0.0001901361,0.000266451,0.00021585,0.000222504],"domain_scores_gemma":[0.9995661,0.00004259152,0.0001329946,0.0001921003,0.00004267207,0.00002352008],"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":[0.00003950864,0.0002028444,0.003578434,0.0001361141,0.00002596574,9.805547e-7,0.00178326,0.00001670468,0.001867234,0.9585668,0.002710315,0.03107188],"study_design_scores_gemma":[0.007208598,0.001125602,0.01561565,0.002439603,0.000102069,0.00001073328,0.002016525,0.07961056,0.6091914,0.2479075,0.03335522,0.001416627],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5855262,0.0004917268,0.2196287,0.006164803,0.003724011,0.008997007,0.0001024821,0.0006913387,0.1746737],"genre_scores_gemma":[0.9029351,0.00001712346,0.09624768,0.0003162911,0.00004031037,0.0001217054,0.000002547318,0.000009812964,0.000309451],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7106593,"threshold_uncertainty_score":0.2886944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06770205945613778,"score_gpt":0.2641757924783974,"score_spread":0.1964737330222596,"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."}}