{"id":"W186600092","doi":"","title":"SgmlQL + XGQL = powerful XML pattern-matching and data-manipulation in a single language","year":2000,"lang":"en","type":"article","venue":"RIAO Conference","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; XML; Document Structure Description; Efficient XML Interchange; Information retrieval; XML validation; Streaming XML; XML Schema Editor; SGML; Matching (statistics); XML Schema (W3C); Pattern matching; Natural language processing; XML Encryption; Programming language; 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":[0.0002140735,0.0001214971,0.0001615726,0.00005516956,0.00005567768,0.0001242774,0.0004276179,0.00003777187,0.0001257396],"category_scores_gemma":[0.00002697345,0.0001124466,0.00001037684,0.0001355628,0.00003033009,0.001365911,0.0002727149,0.0001027947,0.00006345695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001906756,"about_ca_system_score_gemma":0.0000326229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009852633,"about_ca_topic_score_gemma":0.001045189,"domain_scores_codex":[0.9989043,0.00006337754,0.0002190232,0.0004523496,0.0001520196,0.0002088801],"domain_scores_gemma":[0.9990067,0.00006116981,0.00005959577,0.0007950969,0.00002176111,0.00005569082],"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":[0.00001920883,0.000121798,0.003658192,0.0000926483,0.00001327001,0.0001789397,0.01176722,0.0002512138,0.008576782,0.0793941,0.000185231,0.8957414],"study_design_scores_gemma":[0.006205497,0.0007069472,0.125614,0.00292255,0.00004502444,0.0006031839,0.004914623,0.6062206,0.006241572,0.02459717,0.2175565,0.004372321],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4017173,0.0002003035,0.5953513,0.0002156488,0.0001063612,0.0001270319,0.00004307656,0.00008372837,0.002155203],"genre_scores_gemma":[0.9842345,0.00002653016,0.01524088,0.0001257279,0.00004561784,0.000003676018,0.00006509926,0.000006579952,0.0002513762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.891369,"threshold_uncertainty_score":0.4585438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0390225757765091,"score_gpt":0.2871074574610454,"score_spread":0.2480848816845363,"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."}}