{"id":"W1973262365","doi":"10.1016/j.jmb.2005.05.037","title":"Armadillo: Domain Boundary Prediction by Amino Acid Composition","year":2005,"lang":"en","type":"article","venue":"Journal of Molecular Biology","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":60,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital","funders":"","keywords":"Linker; Armadillo; Computational biology; Protein domain; Domain (mathematical analysis); Amino acid; Peptide sequence; Sequence (biology); Biological system; Computer science; Biology; Genetics; Mathematics; Cell 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004088524,0.000160577,0.0002041474,0.00009155841,0.00007678151,0.00002477922,0.0002446867,0.0002462008,0.00003272763],"category_scores_gemma":[0.00006194597,0.0001425943,0.0001571697,0.00006518043,0.0001257688,0.00001044213,0.00007094484,0.0002809588,0.00001418159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004074957,"about_ca_system_score_gemma":0.00006798414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001358092,"about_ca_topic_score_gemma":0.000001001609,"domain_scores_codex":[0.9987867,0.0001746851,0.0005266598,0.0001422477,0.000142932,0.0002268083],"domain_scores_gemma":[0.9991075,0.000008876497,0.0004320033,0.0002054972,0.0001403552,0.0001057439],"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":[0.000111601,0.00007068008,0.001864251,0.000009056108,0.0001030307,0.000005631355,0.00004094394,0.0002193289,0.9843567,0.0001363546,0.008405589,0.004676847],"study_design_scores_gemma":[0.00134678,0.001495552,0.0006294141,0.00001960246,0.00004484298,0.0009138084,0.00003005346,0.0002602485,0.5703238,0.0003001985,0.4244257,0.0002100861],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8630968,0.002469805,0.1314321,0.00113693,0.0003069923,0.00009736006,0.00004375596,0.00001094738,0.0014053],"genre_scores_gemma":[0.973782,0.0001952748,0.02371919,0.001417107,0.0004596969,0.000002680077,0.0003387057,0.00002067282,0.00006466168],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4160201,"threshold_uncertainty_score":0.5814825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002839061238249694,"score_gpt":0.2474695688552085,"score_spread":0.2446305076169588,"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."}}