{"id":"W2489372234","doi":"","title":"A Graphical XQuery Language Using Nested Windows","year":2004,"lang":"en","type":"article","venue":"","topic":"Hermeneutics and Narrative Identity","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"XQuery; Computer science; Programming language; Semantics (computer science); Syntax; Graphical user interface; XML; Artificial intelligence; World Wide Web; Document Structure Description","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":[0.005191857,0.001100672,0.0007014962,0.00088614,0.0006594952,0.003910559,0.002173996,0.00106922,0.01568389],"category_scores_gemma":[0.006513977,0.001112489,0.001105952,0.001102649,0.001669015,0.005602676,0.00306635,0.002721844,0.005282504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007225037,"about_ca_system_score_gemma":0.001264113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002708499,"about_ca_topic_score_gemma":0.001597782,"domain_scores_codex":[0.9963776,0.001191003,0.0006090481,0.0005888323,0.00100578,0.0002275742],"domain_scores_gemma":[0.9968927,0.001817621,0.0002387438,0.0003717317,0.0004743144,0.0002048747],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001661935,0.0001753656,0.001270659,0.001526709,0.000147636,0.001472847,0.004316232,0.01024417,0.04199183,0.6267673,0.1156921,0.1947333],"study_design_scores_gemma":[0.0004784998,0.000265637,0.0005952034,0.000586887,0.0001038697,0.001358695,0.0006494831,0.09837977,0.04434644,0.1934522,0.6595441,0.0002392639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002603179,0.0002547191,0.9609441,0.0004245499,0.0000985519,0.0002641191,0.001376891,0.0281262,0.005907745],"genre_scores_gemma":[0.1119468,0.001172443,0.8393826,0.001173502,0.0001913256,0.00144315,0.007846852,0.01587949,0.02096388],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01568389,"threshold_uncertainty_score":0.05246782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03577361830464785,"score_gpt":0.2609403834064491,"score_spread":0.2251667651018013,"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."}}