{"id":"W4214690579","doi":"10.1109/wi.2004.10048","title":"A Fast Tree Pattern Matching Algorithm for XML Query","year":2005,"lang":"en","type":"article","venue":"IEEE/WIC/ACM International Conference on Web Intelligence (WI'04)","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Pattern matching; Computer science; XML; Matching (statistics); Tree (set theory); Tree decomposition; Algorithm; Theoretical computer science; Mathematics; Artificial intelligence; Combinatorics","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":[0.0009786106,0.000804678,0.0008554608,0.001993301,0.001091613,0.001406744,0.001686175,0.001179923,0.006676963],"category_scores_gemma":[0.002514892,0.0004888515,0.0009534631,0.003220412,0.0005185572,0.003911487,0.0019517,0.001204304,0.00325564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008773187,"about_ca_system_score_gemma":0.001596984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003017859,"about_ca_topic_score_gemma":0.003047838,"domain_scores_codex":[0.9985912,0.000179486,0.0001732494,0.0003221331,0.0006227107,0.0001111865],"domain_scores_gemma":[0.9992712,0.0001961812,0.00005465818,0.0001595903,0.0002893285,0.00002901169],"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":[0.0003973619,0.0001378594,0.001028709,0.00036475,0.00008058743,0.0002203765,0.0002872522,0.01751649,0.03493442,0.05269085,0.03092959,0.8614118],"study_design_scores_gemma":[0.0002908346,0.0003663883,0.000949759,0.00007586823,0.0000942274,0.00138136,0.0002519047,0.7307529,0.05411244,0.1035743,0.1080169,0.000133114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003026364,0.0002962579,0.9909958,0.0001220815,0.00004793622,0.0001618087,0.0003253554,0.003604615,0.001419844],"genre_scores_gemma":[0.03336503,0.0003004846,0.9610444,0.0001293096,0.00003636223,0.0002705054,0.001698107,0.0003384868,0.002817326],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006676963,"threshold_uncertainty_score":0.02233672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05704593411194284,"score_gpt":0.3251700530208864,"score_spread":0.2681241189089436,"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."}}