{"id":"W2159957898","doi":"10.1109/icde.2009.173","title":"Improving the Effectiveness of XML Retrieval with User Navigation Models","year":2009,"lang":"en","type":"article","venue":"Proceedings - International Conference on Data Engineering","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Information retrieval; XML; Ranking (information retrieval); Markup language; XML Schema (W3C); Document Structure Description; XML database; XML framework; Relevance (law); Search engine; XML validation; Key (lock); Data mining; Database; XML Signature; 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":[0.01000636,0.001601443,0.002157737,0.003729124,0.0007940768,0.003036025,0.001633066,0.002360653,0.001323213],"category_scores_gemma":[0.04591689,0.0006211062,0.001107206,0.002520797,0.0007038859,0.007416854,0.001648729,0.001264387,0.001306509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009711342,"about_ca_system_score_gemma":0.001742039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005476869,"about_ca_topic_score_gemma":0.005694759,"domain_scores_codex":[0.9913549,0.00440307,0.0009165717,0.0007576435,0.002248633,0.0003191994],"domain_scores_gemma":[0.9622054,0.02812021,0.001767866,0.003666245,0.003929857,0.0003105414],"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":[0.002459848,0.001489518,0.015712,0.0009052961,0.0003873634,0.0001381971,0.0006068065,0.1123664,0.0336543,0.004239967,0.004335094,0.8237053],"study_design_scores_gemma":[0.0001579375,0.001113098,0.00449206,0.000049169,0.000276188,0.000298582,0.0001980041,0.9534907,0.03357127,0.003499308,0.00273429,0.0001194332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4202024,0.004370715,0.5535284,0.0008256774,0.000112655,0.0005216509,0.000304122,0.01425151,0.005882969],"genre_scores_gemma":[0.6754262,0.0007305246,0.3208506,0.0001552083,0.000101306,0.0001236398,0.0005419453,0.0003393266,0.001731331],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01000636,"threshold_uncertainty_score":0.05291939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03257726831956353,"score_gpt":0.2618656805024498,"score_spread":0.2292884121828863,"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."}}