{"id":"W2473046152","doi":"10.1002/cpbi.2","title":"Inference of Episodic Changes in Natural Selection Acting on Protein Coding Sequences via CODEML","year":2016,"lang":"en","type":"article","venue":"Current Protocols in Bioinformatics","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Inference; Computer science; Workflow; Bootstrapping (finance); Robustness (evolution); Natural selection; Artificial intelligence; Coding (social sciences); Statistical inference; Machine learning; Selection (genetic algorithm); Data mining; Biology; Database; Statistics; Econometrics; Genetics; Gene; Mathematics","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":[0.0003296738,0.0001435317,0.0001552029,0.0001408438,0.00003263121,0.00001368274,0.0001780324,0.0001086384,0.00000718356],"category_scores_gemma":[0.0002708419,0.0001065031,0.0000323501,0.000200923,0.00008472289,0.00001619186,0.00007239141,0.0001410613,0.000004998378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000631582,"about_ca_system_score_gemma":0.00008765966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004962892,"about_ca_topic_score_gemma":0.0002437139,"domain_scores_codex":[0.9989672,0.00005493048,0.0004217044,0.0001520134,0.0001645178,0.0002396378],"domain_scores_gemma":[0.9994456,0.00002523033,0.0002579443,0.0001612996,0.00007397252,0.00003595791],"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":[0.0002768241,0.0002655201,0.03870133,0.001061386,0.00001575287,5.562171e-7,0.0004873617,0.0006198445,0.6321861,0.001004789,0.00007584041,0.3253048],"study_design_scores_gemma":[0.003879922,0.001739201,0.009185417,0.00513103,0.000007142694,0.00000967334,0.0002653923,0.06704146,0.9056144,0.001222797,0.005068218,0.0008353481],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8829876,0.0001108152,0.07137665,0.0002854769,0.0003026251,0.04402296,0.00004035567,0.00004485856,0.0008286278],"genre_scores_gemma":[0.9905341,0.00004829111,0.002284426,0.00002216271,0.00004161907,0.006998016,0.00001801781,0.000008091076,0.00004530399],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3244694,"threshold_uncertainty_score":0.4343071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02838981684700015,"score_gpt":0.3388800348403445,"score_spread":0.3104902179933443,"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."}}