{"id":"W6929022606","doi":"10.48448/xb6h-0942","title":"Data-Efficient Auto-Regressive Document Retrieval for Fact Verification","year":2022,"lang":"en","type":"other","venue":"Underline Science Inc.","topic":"Genetics, Aging, and Longevity in Model Organisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Annotation; Document retrieval; Task (project management); Question answering; Component (thermodynamics); Precision and recall; Natural language; Code (set theory); Vector space model","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007544254,0.0002849776,0.0002217952,0.0001971124,0.0003260882,0.000107991,0.001705899,0.0001914188,0.0007223992],"category_scores_gemma":[0.0002211735,0.0002735012,0.00006256423,0.0002226841,0.0004864299,0.000005109788,0.0008544405,0.0001610181,0.00002867019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000107454,"about_ca_system_score_gemma":0.0007062897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005024245,"about_ca_topic_score_gemma":0.0000620473,"domain_scores_codex":[0.9974636,0.00004457069,0.0002707171,0.001186737,0.0005946183,0.000439697],"domain_scores_gemma":[0.9977515,0.00001412781,0.000317553,0.00166746,0.0001182142,0.0001311417],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001388769,0.0005366469,0.0001308235,0.0002100391,0.000233057,0.000007437597,0.0004598822,0.005778096,0.2654768,0.001420848,0.7134633,0.01214422],"study_design_scores_gemma":[0.0004561858,0.0002685231,0.00006366173,0.00001604066,0.00006577473,0.000007419978,0.0001536449,0.009078633,0.014012,0.00008843024,0.9753384,0.0004512926],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.0373887,0.01841206,0.7408566,0.004650675,0.01908615,0.01391858,0.0184475,0.0009750731,0.1462646],"genre_scores_gemma":[0.3808413,0.001738764,0.0724952,0.002144786,0.004363794,0.0002310662,0.03483851,0.001346166,0.5020004],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6683614,"threshold_uncertainty_score":0.9999717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03136447525108101,"score_gpt":0.3146576835269074,"score_spread":0.2832932082758264,"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."}}