{"id":"W2043304335","doi":"10.1110/ps.036442.108","title":"Fragment‐HMM: A new approach to protein structure prediction","year":2008,"lang":"en","type":"article","venue":"Protein Science","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Hidden Markov model; Fragment (logic); Computer science; Benchmark (surveying); Protein structure prediction; Position (finance); Selection (genetic algorithm); Simple (philosophy); Artificial intelligence; Algorithm; Machine learning; Computational biology; Data mining; Pattern recognition (psychology); Protein structure; Biology; Geography","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":[0.0002235626,0.0002040646,0.0001366283,0.0001078404,0.0003813977,0.00005625919,0.0006718951,0.0001436307,0.00001867564],"category_scores_gemma":[0.0002116964,0.0001786921,0.00005120444,0.0006163301,0.0003224639,0.0000227271,0.0002844844,0.0001511101,0.0000192708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004563396,"about_ca_system_score_gemma":0.0005550754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004997224,"about_ca_topic_score_gemma":0.000005429315,"domain_scores_codex":[0.9980018,0.00002923633,0.0002056456,0.0007865249,0.0005226893,0.0004541037],"domain_scores_gemma":[0.9988406,0.000001387519,0.00007618502,0.0006301567,0.00012899,0.0003226479],"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":[0.00004648103,0.00002453548,0.000309655,0.00001158899,0.000005890312,0.000001687544,0.0001328497,0.000224896,0.9954231,0.0006924809,0.0007380228,0.002388781],"study_design_scores_gemma":[0.0004792842,0.000327938,0.002581099,0.00002408802,0.000005435741,0.0001127718,0.00003037354,0.0005597414,0.9781895,0.001968055,0.01535506,0.0003667087],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9235608,0.0001125447,0.06835255,0.000155615,0.0001092451,0.001711754,0.00003193375,0.00004659599,0.005918961],"genre_scores_gemma":[0.9328386,0.000004046985,0.06303108,0.0002417371,0.0003208534,0.0001238757,0.00002875716,0.00001788777,0.003393123],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01723368,"threshold_uncertainty_score":0.7286854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01077955891816665,"score_gpt":0.2324435986825459,"score_spread":0.2216640397643792,"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."}}