{"id":"W2279033101","doi":"10.1007/978-3-540-85902-4_3","title":"XML Retrieval by Improving Structural Relevance Measures Obtained from Summary Models","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Relevance (law); Information retrieval; Ranking (information retrieval); XML; Rank (graph theory); Data mining; Document Structure Description; 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":[0.002903081,0.001339273,0.001851116,0.008031961,0.0005581636,0.002411691,0.001505983,0.00169522,0.004588749],"category_scores_gemma":[0.01708055,0.0005917986,0.001792481,0.004502744,0.0004503244,0.006076356,0.001281796,0.001300337,0.002988223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007873267,"about_ca_system_score_gemma":0.00103111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002349792,"about_ca_topic_score_gemma":0.003516511,"domain_scores_codex":[0.9970126,0.0008946572,0.0004025577,0.0005487613,0.0009628166,0.0001785303],"domain_scores_gemma":[0.9923078,0.004365182,0.0005650405,0.001258136,0.001362961,0.0001408574],"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":[0.0016218,0.0003612958,0.005204436,0.001043575,0.0005228235,0.0002688666,0.00023627,0.04139774,0.03649137,0.01141878,0.02102144,0.8804116],"study_design_scores_gemma":[0.0001609597,0.0006642358,0.00381102,0.00009927509,0.0008873868,0.0005142503,0.0001317481,0.9299532,0.02692029,0.02695329,0.009809843,0.00009445436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08981417,0.007291164,0.8852141,0.0008103167,0.0003128954,0.0002712247,0.00244086,0.00952149,0.004323849],"genre_scores_gemma":[0.6088275,0.002784924,0.3700568,0.0003909301,0.001067763,0.0002551615,0.01050706,0.001076589,0.005033194],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008031961,"threshold_uncertainty_score":0.01535308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01870901423863776,"score_gpt":0.2211303684102863,"score_spread":0.2024213541716486,"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."}}