{"id":"W2123080243","doi":"10.1007/s00778-009-0159-9","title":"Schema mapping and query translation in heterogeneous P2P XML databases","year":2009,"lang":"en","type":"article","venue":"The VLDB Journal","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia Hospital","funders":"","keywords":"Computer science; XQuery; Information retrieval; Scalability; Schema (genetic algorithms); Document Structure Description; Metadata; XML; Query language; XML Schema Editor; Database; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000459147,0.00008635406,0.0001120532,0.0000864539,0.0001898128,0.00007687136,0.0002014204,0.00001609086,0.000004559096],"category_scores_gemma":[0.0000255198,0.00005731219,0.00002675005,0.0001663937,0.00002941489,0.0008220204,0.00004209443,0.0001784654,0.000003608148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001646498,"about_ca_system_score_gemma":0.00002866466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000180982,"about_ca_topic_score_gemma":0.00002725631,"domain_scores_codex":[0.9992227,0.00008047922,0.0002249856,0.0001372857,0.0001526077,0.0001819285],"domain_scores_gemma":[0.9995177,0.00006739565,0.00008926132,0.0002489203,0.00002126133,0.00005546201],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005708184,0.0001106775,0.00150474,0.00003911663,0.00003546909,0.000504077,0.006050105,0.002447234,0.03278927,0.0934072,0.0005136825,0.8625413],"study_design_scores_gemma":[0.007625407,0.001025431,0.05637302,0.003017981,0.00005781197,0.04412436,0.002720037,0.2043787,0.0432257,0.05064587,0.5838602,0.002945482],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.175739,0.00261762,0.8195325,0.00173229,0.0001396572,0.00007527315,0.000004721637,0.00002800559,0.0001309913],"genre_scores_gemma":[0.9088008,0.0003449896,0.09022402,0.0004130528,0.0001857701,0.00000172585,0.000002555812,0.000004942448,0.0000221016],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8595958,"threshold_uncertainty_score":0.2337123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04612929082107779,"score_gpt":0.2739027125305562,"score_spread":0.2277734217094784,"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."}}