{"id":"W2188953292","doi":"","title":"Discovering and using semantics for database schemas","year":2007,"lang":"en","type":"article","venue":"TSpace","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Information retrieval; Database schema; Conceptual schema; Data integration; Data exchange; Semi-structured model; Information schema; Database; Relational database; XML Schema Editor; Schema (genetic algorithms); XML; Database design; World Wide Web","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":[0.02620951,0.001422157,0.001516923,0.009106625,0.003060285,0.01561348,0.005359144,0.002672668,0.00253345],"category_scores_gemma":[0.06749067,0.002766004,0.004336574,0.006735604,0.005064268,0.03814402,0.008951604,0.004715551,0.001236049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003681667,"about_ca_system_score_gemma":0.007585525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005199357,"about_ca_topic_score_gemma":0.005205663,"domain_scores_codex":[0.970813,0.01072654,0.004706085,0.003261445,0.009655518,0.0008374594],"domain_scores_gemma":[0.9603746,0.01739028,0.002665765,0.01179384,0.007084289,0.0006911914],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006879682,0.00009059953,0.00223373,0.0006070224,0.0001366171,0.0003484584,0.003556676,0.009794054,0.003034302,0.8441699,0.004038633,0.1319212],"study_design_scores_gemma":[0.00006074901,0.00008017707,0.0004441118,0.001025915,0.000161329,0.0008356292,0.002590568,0.1127905,0.01650813,0.7035245,0.1618541,0.0001242214],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00466784,0.0006867595,0.9891399,0.001200535,0.00006989652,0.000246835,0.0003774685,0.001317689,0.002293038],"genre_scores_gemma":[0.04587433,0.001244224,0.9493285,0.0003114676,0.00005841975,0.0002277518,0.001402679,0.0004678532,0.001084675],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02620951,"threshold_uncertainty_score":0.1386108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04763921371606723,"score_gpt":0.3709369676324673,"score_spread":0.3232977539164,"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."}}