{"id":"W1554673294","doi":"","title":"Benefits of path summaries in an XML query optimizer supporting multiple access methods","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Heuristics; Exploit; Query optimization; Path (computing); XML; Data mining; Range (aeronautics); Path expression; Range query (database); Index (typography); Information retrieval; Web search query; Web query classification; Query language; Search engine; 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":[0.0008438285,0.0001400722,0.0002896334,0.0001176243,0.00006192761,0.00007345212,0.0005667357,0.0000509793,0.00003556267],"category_scores_gemma":[0.0001793932,0.0001155357,0.00004351049,0.0003056033,0.00004681883,0.003662226,0.0004956798,0.0000926057,0.000003036364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002330431,"about_ca_system_score_gemma":0.0000571286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006585699,"about_ca_topic_score_gemma":0.002044065,"domain_scores_codex":[0.9984893,0.0001397248,0.0005309221,0.00036744,0.0001676817,0.0003049068],"domain_scores_gemma":[0.998776,0.0002409055,0.0002025361,0.000604751,0.00009391017,0.00008196449],"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":[0.00003786371,0.0002620494,0.06227683,0.0001301995,0.00001779854,0.00001019378,0.00296796,0.07282662,0.002358042,0.2710371,0.0003004013,0.5877749],"study_design_scores_gemma":[0.002005922,0.0002109325,0.05023129,0.0002493715,0.00001035161,0.00002794485,0.001165227,0.7336062,0.1738654,0.001178477,0.03627318,0.001175721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05495439,0.0002177611,0.9435493,0.0001602283,0.0001256668,0.0001465862,0.00002125497,0.00009727907,0.0007275516],"genre_scores_gemma":[0.1706461,0.00002060707,0.8290061,0.0001303342,0.000048985,0.00001655962,0.00001352026,0.000008268622,0.00010954],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6607796,"threshold_uncertainty_score":0.4711407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05331633857844409,"score_gpt":0.3804662029014854,"score_spread":0.3271498643230413,"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."}}