{"id":"W2142018130","doi":"","title":"DalTREC 2006 QA System Jellyfish: Regular Expressions Mark-and-Match Approach to Question Answering","year":2006,"lang":"en","type":"article","venue":"Text REtrieval Conference","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Question answering; Rewriting; Computer science; Jellyfish; Robustness (evolution); Regular expression; Artificial intelligence; Architecture; Natural language processing; Information retrieval; Programming language","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":[0.004331584,0.001299345,0.001660376,0.002724305,0.001161298,0.002632555,0.003740552,0.002358036,0.02174246],"category_scores_gemma":[0.00815233,0.0009049403,0.001503317,0.001238558,0.001127447,0.005056226,0.003380413,0.003140947,0.01675961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002149221,"about_ca_system_score_gemma":0.002968021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01227386,"about_ca_topic_score_gemma":0.01010971,"domain_scores_codex":[0.9972149,0.0005754703,0.0002944801,0.0008810735,0.0008640728,0.0001699373],"domain_scores_gemma":[0.9959078,0.0008428835,0.0002050976,0.001299296,0.00152847,0.0002164846],"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":[0.001244985,0.0007395202,0.003054888,0.001546126,0.0003236627,0.000505943,0.001311093,0.01427375,0.1093497,0.02940761,0.3271458,0.511097],"study_design_scores_gemma":[0.0004850458,0.0008989056,0.003064172,0.0001479603,0.0002624512,0.001148753,0.0003465562,0.3721124,0.1503271,0.03501696,0.4357969,0.0003927768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02156454,0.001141954,0.7078927,0.002119879,0.0005918153,0.001477938,0.008540386,0.2376196,0.01905124],"genre_scores_gemma":[0.1391872,0.0006614957,0.7768408,0.001860171,0.0003057375,0.000841115,0.02791592,0.006667329,0.04572025],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02174246,"threshold_uncertainty_score":0.07273579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02211898084258234,"score_gpt":0.2386022074487429,"score_spread":0.2164832266061605,"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."}}