{"id":"W2781990454","doi":"10.1002/wics.1423","title":"Computational methods for birth‐death processes","year":2018,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Diffusion and Search Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences; National Human Genome Research Institute; Dalhousie University; National Institutes of Health; National Science Foundation","keywords":"Inference; Birth–death process; Computer science; Statistical inference; Simple (philosophy); Hidden Markov model; Poisson distribution; Artificial intelligence; Algorithm; Machine learning; Statistical physics; Mathematics; Statistics; Population; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001020296,0.0007304958,0.001668253,0.000199017,0.0003963977,0.0001390224,0.0007157903,0.0003375901,0.000101556],"category_scores_gemma":[0.0008660763,0.0006072213,0.0006160726,0.0003183155,0.0002498054,0.00001085022,0.0009416194,0.0002667356,0.000152922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009757283,"about_ca_system_score_gemma":0.001027609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.719701e-7,"about_ca_topic_score_gemma":0.000006331988,"domain_scores_codex":[0.9961218,0.0006028924,0.001513039,0.001004659,0.000299316,0.0004582823],"domain_scores_gemma":[0.9967776,0.000839947,0.0008534886,0.0004297122,0.0008634096,0.0002358257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000403505,0.000186323,0.000001476852,0.02442086,0.0002576128,0.000003545071,0.00005225613,0.0005815538,4.262646e-7,0.002070989,0.07613282,0.8962518],"study_design_scores_gemma":[0.0002736069,0.0005566166,0.00000153359,0.005442367,0.0003055779,0.00009699618,0.00001022038,0.01290363,2.26646e-7,0.0160613,0.963712,0.000635908],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[3.085769e-7,0.5034556,0.492453,0.00001644467,0.0002435754,0.001205448,0.002478519,0.00001656065,0.0001305219],"genre_scores_gemma":[2.735444e-7,0.5725594,0.4069987,0.0001054332,0.0004201458,0.0004334666,0.01841305,0.0000799341,0.0009895401],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8956159,"threshold_uncertainty_score":0.9996379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08468246692179184,"score_gpt":0.4729340910250185,"score_spread":0.3882516241032267,"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."}}