{"id":"W2083366092","doi":"10.1186/1471-2105-10-222","title":"PCI-SS: MISO dynamic nonlinear protein secondary structure prediction","year":2009,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Protein secondary structure; Conventional PCI; Interface (matter); Nonlinear system; Machine learning; Artificial intelligence; Data mining; Biology; Parallel computing; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000102579,0.0002535506,0.0001795705,0.00006703689,0.0001181004,0.00005804612,0.0002629106,0.0003641417,0.00003117583],"category_scores_gemma":[0.00007177009,0.0002239232,0.0001076794,0.0001214845,0.0000629913,0.00002204302,0.00006747486,0.0002310962,0.00001522648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002929531,"about_ca_system_score_gemma":0.0001918603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002100335,"about_ca_topic_score_gemma":0.00003541226,"domain_scores_codex":[0.99878,0.00002264236,0.0004639956,0.0002132219,0.0002025782,0.0003176274],"domain_scores_gemma":[0.9990984,0.000004180612,0.0001902003,0.0005088827,0.00008770199,0.0001106882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007042698,0.0002158176,0.001436233,0.0009395423,0.0002197426,0.000009911801,0.0006728923,0.003832851,0.8058157,0.001147491,0.008038717,0.1769668],"study_design_scores_gemma":[0.004858378,0.002970922,0.009731277,0.0001924172,0.0001333474,0.0004806631,0.0005718325,0.7399303,0.1134452,0.005968418,0.1198253,0.001891909],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7952151,0.0008574396,0.1939276,0.0002115727,0.0007116834,0.00160998,0.001173293,0.0001829493,0.006110446],"genre_scores_gemma":[0.4907641,0.00009230309,0.5008149,0.001420511,0.0005503288,0.00002333011,0.003605744,0.00005117276,0.002677638],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7360975,"threshold_uncertainty_score":0.9131321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003857221874291273,"score_gpt":0.215927855935394,"score_spread":0.2120706340611027,"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."}}