{"id":"W2025155398","doi":"10.1016/j.datak.2009.10.001","title":"Skyline queries with constraints: Integrating skyline and traditional query operators","year":2009,"lang":"en","type":"article","venue":"Data & Knowledge Engineering","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Skyline; Computer science; Pruning; Class (philosophy); Space (punctuation); Operator (biology); Database; Computation; Information retrieval; Data mining; Theoretical computer science; Algorithm; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005161947,0.001047571,0.002088022,0.00206789,0.0008346182,0.003783,0.00361251,0.001102574,0.00400952],"category_scores_gemma":[0.01276149,0.0007389361,0.00106857,0.005667597,0.001130295,0.01091166,0.004595242,0.00148146,0.000847752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007951144,"about_ca_system_score_gemma":0.001574767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004289238,"about_ca_topic_score_gemma":0.008845294,"domain_scores_codex":[0.9949195,0.001276135,0.0005099623,0.0007555536,0.002289212,0.0002496568],"domain_scores_gemma":[0.990529,0.003890262,0.0007482592,0.002830372,0.001575641,0.0004264128],"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":[0.002272796,0.0006525393,0.009341892,0.001955671,0.0005765926,0.0008272638,0.001435663,0.05770215,0.02322547,0.1114787,0.0823155,0.7082158],"study_design_scores_gemma":[0.0003778154,0.0003532865,0.001790763,0.0001665985,0.0002665593,0.0008587037,0.0006025313,0.7883137,0.01802688,0.11489,0.07421883,0.0001343086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0148405,0.001031833,0.9720415,0.0005767847,0.00007530432,0.0001646072,0.001328311,0.007505117,0.002436156],"genre_scores_gemma":[0.1648113,0.001145198,0.8260269,0.0003999732,0.0002316358,0.0001830104,0.003568288,0.001783587,0.001850135],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005161947,"threshold_uncertainty_score":0.02729928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02709674014065473,"score_gpt":0.2344229936000985,"score_spread":0.2073262534594438,"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."}}