{"id":"W2444034415","doi":"10.1016/j.ymeth.2016.06.011","title":"Corrigendum to “Text as data: Using text-based features for proteins representation and for computational prediction of their characteristics” [Methods 74 (2015) 54–64]","year":2016,"lang":"en","type":"erratum","venue":"Methods","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Mount Sinai Hospital; University of Toronto; Queen's University","funders":"U.S. National Library of Medicine; National Institutes of Health","keywords":"Representation (politics); Computer science; Computational biology; Artificial intelligence; Natural language processing; Biology","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":[0.003752126,0.002871042,0.003139491,0.00604639,0.003547196,0.005518077,0.00498549,0.005817279,0.2027106],"category_scores_gemma":[0.05178019,0.001710815,0.003123224,0.003930838,0.001954304,0.003561223,0.003779914,0.008491199,0.1381962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004433685,"about_ca_system_score_gemma":0.006518323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0368365,"about_ca_topic_score_gemma":0.05618915,"domain_scores_codex":[0.994589,0.0007142811,0.0008995855,0.0007705693,0.002570571,0.0004560645],"domain_scores_gemma":[0.9671018,0.005547801,0.001189083,0.002761205,0.02198904,0.001411158],"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":[0.00001249088,0.000006674654,0.00002134303,0.00005397163,0.000006467415,0.00004123718,0.000005689871,0.00003193298,0.0000450845,0.0003047207,0.996323,0.003147444],"study_design_scores_gemma":[0.00003656275,0.00002521203,0.0008806894,0.0002346724,0.00004772039,0.000178767,0.00004084179,0.0005905959,0.0006101535,0.002453313,0.9948518,0.00004971808],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.0001221878,0.0009254906,0.001802844,0.03750023,0.9472525,0.00005203614,0.004114541,0.001076913,0.0071532],"genre_scores_gemma":[0.005793973,0.00656722,0.01237926,0.09418901,0.1817773,0.0003797277,0.02032429,0.004302625,0.6742867],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.2027106,"threshold_uncertainty_score":0.6781343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08380043872700878,"score_gpt":0.4424589382317048,"score_spread":0.358658499504696,"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."}}