{"id":"W2037133727","doi":"10.1145/2463676.2463687","title":"A query answering system for data with evolution relationships","year":2013,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Computer science; Schema (genetic algorithms); Database schema; Query language; View; Schema evolution; Information retrieval; Probabilistic logic; Database theory; Database; Probabilistic database; Relational database; Database design; 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":[0.006208019,0.001129959,0.001733469,0.003187516,0.002414143,0.004636139,0.003897291,0.003673306,0.005998621],"category_scores_gemma":[0.01740802,0.001196491,0.001677826,0.004179379,0.001322156,0.01092244,0.004739229,0.002202154,0.002650424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001529628,"about_ca_system_score_gemma":0.001716936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004784657,"about_ca_topic_score_gemma":0.003541724,"domain_scores_codex":[0.9950554,0.001178403,0.0007306462,0.001086872,0.001643886,0.0003047542],"domain_scores_gemma":[0.9925988,0.003634676,0.0005124428,0.001763046,0.001150217,0.0003408251],"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":[0.002501599,0.0007409398,0.01367288,0.001645565,0.0006071264,0.002827942,0.00368331,0.04141299,0.05607039,0.152051,0.1503353,0.5744511],"study_design_scores_gemma":[0.0004475625,0.0004864446,0.003034662,0.0001827877,0.0003823205,0.002152306,0.0007832989,0.671019,0.03084031,0.1331792,0.157253,0.0002391842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02156709,0.0007524725,0.9042585,0.002312099,0.000170911,0.0006032811,0.003826604,0.06198166,0.004527373],"genre_scores_gemma":[0.2474237,0.0009504459,0.7258273,0.002006707,0.0004558707,0.001066537,0.0132146,0.002322956,0.006731876],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006208019,"threshold_uncertainty_score":0.03283155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0533440084306374,"score_gpt":0.2352773101337861,"score_spread":0.1819333017031486,"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."}}