{"id":"W4408609844","doi":"10.1101/2025.03.17.643624","title":"randPedPCA: Rapid approximation of principal components from large pedigrees","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pedigree chart; Principal component analysis; Principal (computer security); Mathematics; Computer science; Artificial intelligence; Biology; Genetics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005064109,0.000483557,0.0008361368,0.0002862592,0.0001649478,0.00009207914,0.0006720828,0.0004995184,0.0001671482],"category_scores_gemma":[0.0002124283,0.0005231978,0.0002691595,0.000359577,0.00008686246,0.0001033971,0.0005438582,0.0005915962,0.0000429521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001402009,"about_ca_system_score_gemma":0.0002438512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004280473,"about_ca_topic_score_gemma":0.000003043008,"domain_scores_codex":[0.9972367,0.0001774587,0.0009614057,0.0007710357,0.0004736815,0.0003797417],"domain_scores_gemma":[0.9966279,0.0002973846,0.0008406634,0.001525748,0.0005455236,0.0001627775],"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":[0.0002558208,0.00401993,0.01237714,0.004289772,0.001443868,0.00002142329,0.0002388976,0.00008519631,0.8075593,0.164082,0.0056146,0.00001207945],"study_design_scores_gemma":[0.008943456,0.0001062068,0.2815228,0.004362368,0.001953904,3.03914e-8,0.0000418592,0.01063969,0.6622303,0.00307997,0.02363048,0.00348896],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9583685,0.0003915988,0.03477064,0.0002505769,0.0006415889,0.001442904,0.003456665,0.0005584437,0.0001190526],"genre_scores_gemma":[0.9575267,0.0001040252,0.04160387,0.00009100672,0.0002608091,0.0003234138,0.000007500847,0.00006391239,0.00001871261],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2691456,"threshold_uncertainty_score":0.9997219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03490179435241681,"score_gpt":0.2746414252278181,"score_spread":0.2397396308754013,"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."}}