{"id":"W2804120765","doi":"10.1002/gepi.22129","title":"SimPEL: Simulation‐based power estimation for sequencing studies of low‐prevalence conditions","year":2018,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; Rare Disease Foundation; University of Calgary","keywords":"Statistical power; Leverage (statistics); Executable; Computer science; Sample size determination; Ranking (information retrieval); Genetic association; Data mining; Computational biology; Statistics; Genotype; Machine learning; Biology; Genetics; Mathematics; Gene; Single-nucleotide polymorphism","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001383451,0.0002017124,0.0005008163,0.00009119526,0.0001890621,0.000002394936,0.0002040189,0.0002937861,0.00005490566],"category_scores_gemma":[0.01395733,0.0001934063,0.0001768431,0.0001173245,0.0005936273,0.000003664362,0.00007333965,0.0000637077,0.00001707432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004779395,"about_ca_system_score_gemma":0.0001712943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008874698,"about_ca_topic_score_gemma":0.00001685178,"domain_scores_codex":[0.9976556,0.0004895336,0.0008572935,0.000494884,0.00006662115,0.0004360525],"domain_scores_gemma":[0.9961603,0.002101964,0.0005273502,0.0004797922,0.0006511046,0.00007948527],"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":[0.000124464,0.00007790208,0.1000676,0.0002719186,0.0003394954,4.933271e-7,0.0002475466,0.8145641,0.075125,0.001132874,0.00488784,0.003160781],"study_design_scores_gemma":[0.00276235,0.005828494,0.2470918,0.0002012742,0.0003374249,0.00001527098,0.0006145057,0.6581279,0.03024377,0.0492483,0.004499136,0.001029755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5999213,0.001087361,0.3978319,0.0003148055,0.0002941983,0.0003635025,0.0000870447,0.00001539786,0.00008444319],"genre_scores_gemma":[0.870244,0.0001016611,0.127836,0.001205089,0.000222397,0.0001065134,0.0001343245,0.00002100913,0.0001290421],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2703226,"threshold_uncertainty_score":0.9943485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07501922967358635,"score_gpt":0.4043770629575013,"score_spread":0.329357833283915,"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."}}