{"id":"W2418468785","doi":"","title":"A method for verifying a vector-based text classification system.","year":2008,"lang":"en","type":"article","venue":"PubMed","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Computer science; Search engine indexing; Java; Suite; Set (abstract data type); Lisp; Similarity (geometry); Data mining; Index (typography); Information retrieval; Vector space model; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002959582,0.00009105396,0.0001193365,0.00003189867,0.0001077745,0.000009338236,0.0001253301,0.0001609588,0.000001166029],"category_scores_gemma":[0.0002409655,0.00007915135,0.00008286381,0.00007004762,0.00007213277,0.000001101669,0.00001974382,0.00004562477,0.000002874106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002449307,"about_ca_system_score_gemma":0.00004724258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008991866,"about_ca_topic_score_gemma":0.000003050576,"domain_scores_codex":[0.9992077,0.00005478647,0.0001424807,0.0002597029,0.00008577535,0.0002496032],"domain_scores_gemma":[0.9995538,0.00004920345,0.00006358838,0.0002046987,0.0000500376,0.0000786551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000565917,0.0001815774,0.003810036,0.0003624975,0.0001599568,0.000007983022,0.0001008515,0.00003232639,0.1152301,0.0009686112,0.02196446,0.8566157],"study_design_scores_gemma":[0.003143135,0.0003294979,0.2395553,0.00003119719,0.00007754596,0.00009172554,0.0004702173,0.005165779,0.1754196,0.00003431159,0.5750615,0.0006201881],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2825117,0.001707102,0.7066745,0.001667064,0.0007159665,0.001743189,0.00006117813,0.000256763,0.004662564],"genre_scores_gemma":[0.9665625,0.00001053457,0.03062663,0.0001855677,0.0002148505,0.001985664,0.00006020594,0.00001415435,0.0003398737],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8559955,"threshold_uncertainty_score":0.3227699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07073376003347205,"score_gpt":0.2900451600503665,"score_spread":0.2193114000168944,"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."}}