{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004536607,0.001496331,0.0008838144,0.00121135,0.0007170909,0.001885623,0.001203911,0.001070212,0.001756776],"category_scores_gemma":[0.01550117,0.0007346467,0.0005051695,0.002188844,0.0004522878,0.003408844,0.00113826,0.0007321897,0.0004180149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008341077,"about_ca_system_score_gemma":0.00098301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005313924,"about_ca_topic_score_gemma":0.005945737,"domain_scores_codex":[0.9942383,0.002848987,0.0005495657,0.0003253483,0.00172447,0.0003132696],"domain_scores_gemma":[0.9870308,0.01012473,0.0006985675,0.0009780348,0.0009521847,0.000215677],"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":[0.01208032,0.001129848,0.0195594,0.001263693,0.0005410093,0.0004230376,0.0009500539,0.282625,0.1179722,0.01173552,0.009593439,0.5421266],"study_design_scores_gemma":[0.0005466628,0.001815848,0.007159377,0.00004311791,0.0004165612,0.000335152,0.0002505318,0.9002346,0.0805397,0.003073791,0.005462898,0.0001218349],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7744703,0.003986528,0.196374,0.0007434417,0.00006014451,0.0003179536,0.000629,0.01897373,0.004445019],"genre_scores_gemma":[0.7369319,0.0007019889,0.2589287,0.00008760876,0.00004889881,0.0001258093,0.0008446608,0.001086603,0.001243791],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005313924,"threshold_uncertainty_score":0.02399218,"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."}}