{"id":"W4406872811","doi":"10.1016/j.infsof.2025.107674","title":"XL-HQL: A HQL query generation method via XLNet and column attention","year":2025,"lang":"en","type":"article","venue":"Information and Software Technology","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"National Natural Science Foundation of China","keywords":"Column (typography); Computer science; Telecommunications","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":[0.001547725,0.001363121,0.0008359286,0.003877709,0.0007175516,0.001854338,0.002033601,0.001045195,0.049037],"category_scores_gemma":[0.00710984,0.0007589589,0.001051586,0.002336544,0.0004645592,0.003059745,0.002712789,0.001074895,0.01371254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001021951,"about_ca_system_score_gemma":0.001462857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008808743,"about_ca_topic_score_gemma":0.01193464,"domain_scores_codex":[0.9984591,0.000384631,0.0001453958,0.0002818476,0.0006024146,0.0001266127],"domain_scores_gemma":[0.9971812,0.001299043,0.0001011718,0.0004670614,0.000824797,0.0001268325],"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":[0.0007900505,0.0002500697,0.004376549,0.001215447,0.0001736242,0.00029456,0.0006799914,0.01121515,0.02430729,0.01603617,0.2657377,0.6749234],"study_design_scores_gemma":[0.0004262543,0.0002666341,0.002278494,0.0001428063,0.0001963005,0.0003137915,0.0007352242,0.7237239,0.05465071,0.02600913,0.1911154,0.0001413592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008365748,0.0003764418,0.8234845,0.000740676,0.0002307256,0.0005997448,0.01333331,0.1447249,0.00814392],"genre_scores_gemma":[0.146247,0.0003006472,0.785828,0.0008541065,0.0002625411,0.0009465844,0.03498964,0.0134695,0.01710186],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.049037,"threshold_uncertainty_score":0.1640451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008798551029385699,"score_gpt":0.2539176403397022,"score_spread":0.2451190893103165,"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."}}