{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0006506285,0.0008091535,0.0007133238,0.0005810588,0.0004948537,0.001047608,0.006499808,0.0003526752,0.00001506669],"category_scores_gemma":[0.0001350898,0.0007449251,0.0001742983,0.0007106204,0.0007424933,0.00291233,0.003004528,0.001069202,0.00002566395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003695883,"about_ca_system_score_gemma":0.0004154865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001531163,"about_ca_topic_score_gemma":0.00003310685,"domain_scores_codex":[0.9937748,0.00005216639,0.0007025849,0.002542399,0.001953973,0.000974016],"domain_scores_gemma":[0.9963844,0.0004688291,0.0004400096,0.002204889,0.0002595786,0.0002423045],"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":[0.00002208808,0.00001616913,0.00001168919,0.00002825026,0.00003170294,0.0001991963,0.0005823469,0.01429543,0.0005699701,0.003149656,0.001175817,0.9799177],"study_design_scores_gemma":[0.0003907949,0.0001028638,0.00002411065,0.0001987892,0.00001270578,0.00002221898,2.061088e-7,0.8932749,0.001325986,0.1000267,0.003640112,0.0009805736],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001173071,0.002995883,0.9910225,0.000456904,0.002361232,0.0004431046,0.0000912391,0.000337181,0.002174694],"genre_scores_gemma":[0.07199304,0.001008851,0.9201005,0.002714624,0.001527867,0.000007938334,0.0002200541,0.0001222808,0.002304864],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9789371,"threshold_uncertainty_score":0.9999894,"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."}}