{"id":"W2129766253","doi":"10.1093/molbev/msn095","title":"Bayesian Inference of Errors in Ancient DNA Caused by Postmortem Degradation","year":2008,"lang":"en","type":"article","venue":"Molecular Biology and Evolution","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Ancient DNA; Biology; Inference; Computational biology; DNA; Markov chain; DNA sequencing; Bayesian probability; Genetics; Algorithm; Evolutionary biology; Computer science; Statistics; Mathematics; Artificial intelligence","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":[0.007507252,0.0005530524,0.001024601,0.001983173,0.0006625715,0.001293735,0.001212122,0.0010518,0.0007098145],"category_scores_gemma":[0.03569923,0.0008218709,0.0006051224,0.00117002,0.001500873,0.001658683,0.001109487,0.001875085,0.000209203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000831016,"about_ca_system_score_gemma":0.0007871689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003067653,"about_ca_topic_score_gemma":0.002787952,"domain_scores_codex":[0.9977695,0.001124623,0.0001181684,0.0005738246,0.0003174317,0.00009648869],"domain_scores_gemma":[0.9769441,0.01835709,0.002191615,0.00118704,0.000893929,0.000426219],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001075667,0.0001380653,0.1445938,0.0005101097,0.0008434763,0.000754763,0.001089713,0.6341397,0.03186383,0.05871368,0.001389822,0.1248874],"study_design_scores_gemma":[0.00006168559,0.00007397041,0.02079841,0.00006146842,0.0001112777,0.0003223032,0.0000899943,0.9172275,0.007092906,0.05319704,0.0008742029,0.00008920136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4275753,0.000736872,0.5702071,0.0001938257,0.00003958933,0.00002413901,0.0002154106,0.0002929224,0.0007148189],"genre_scores_gemma":[0.9150627,0.0004987139,0.08324034,0.0000792194,0.00004812264,0.00003980895,0.0004850699,0.00008619687,0.0004599041],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007507252,"threshold_uncertainty_score":0.03970259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008551831422986777,"score_gpt":0.2415291627474615,"score_spread":0.2329773313244747,"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."}}