{"id":"W2166281503","doi":"","title":"DalTREC 2004: Question Answering using Regular Expression Rewriting","year":2004,"lang":"en","type":"article","venue":"Text REtrieval Conference","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Rewriting; Regular expression; Computer science; Expression (computer science); Question answering; Search engine; Information retrieval; Track (disk drive); Programming language; Natural language processing; World Wide Web; Artificial intelligence; Operating system","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005870951,0.0002382437,0.0002310738,0.0001733229,0.000274776,0.0004193625,0.001038615,0.0001751234,0.00001571429],"category_scores_gemma":[0.0004004231,0.0002255891,0.0000586047,0.0006641574,0.00008455727,0.001338826,0.0003850908,0.0004045802,0.00001652799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002462956,"about_ca_system_score_gemma":0.0002672039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001368804,"about_ca_topic_score_gemma":0.000005166013,"domain_scores_codex":[0.998033,0.00009065986,0.0003406508,0.0006099014,0.000497938,0.0004277749],"domain_scores_gemma":[0.9986324,0.00005046784,0.0002134069,0.0006679422,0.0003064162,0.0001293865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002931313,0.00003945495,0.0001192252,0.00007390835,0.000005217531,0.00008567797,0.000701083,0.0001543359,0.8876539,0.08617448,0.00002493309,0.02493841],"study_design_scores_gemma":[0.0003468691,0.00008439561,0.0000891903,0.001167945,0.000007407546,0.0001099089,0.00004479996,0.009985743,0.90548,0.08211081,0.0001519263,0.0004209684],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09903895,0.001751156,0.897034,0.0003984281,0.0001703141,0.0001752213,0.000001461692,0.001084114,0.0003463882],"genre_scores_gemma":[0.6032013,0.00001441293,0.3965726,0.00007710383,0.00006186849,0.000001747659,0.000002296186,0.0000120352,0.00005662906],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5041624,"threshold_uncertainty_score":0.9199258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02825001249352575,"score_gpt":0.2986974757111274,"score_spread":0.2704474632176017,"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."}}