{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024986,0.001546686,0.001017388,0.001730711,0.0006332606,0.001761071,0.001536489,0.0008539472,0.008280404],"category_scores_gemma":[0.01394123,0.0008994536,0.001608135,0.001792566,0.00076139,0.001454049,0.001963273,0.00189361,0.003847023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007170803,"about_ca_system_score_gemma":0.002450673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01569157,"about_ca_topic_score_gemma":0.01903036,"domain_scores_codex":[0.9988548,0.0004972601,0.00005818382,0.0001897795,0.0002878437,0.0001121743],"domain_scores_gemma":[0.996005,0.002324663,0.00025223,0.0006705222,0.0005818283,0.0001658117],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003986688,0.0001220662,0.004245934,0.0005474226,0.0004192809,0.0006271754,0.0005956711,0.4141148,0.01035711,0.04445091,0.05485945,0.4692615],"study_design_scores_gemma":[0.0000474645,0.00002572435,0.001068147,0.00004186112,0.00002290006,0.00016085,0.00004792046,0.9580517,0.001672497,0.02997686,0.008853586,0.00003043501],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006105908,0.0002741908,0.9867915,0.000228574,0.00005835287,0.00005834019,0.0006233359,0.005077895,0.0007817799],"genre_scores_gemma":[0.103316,0.0006467502,0.8864135,0.0002069418,0.0001027561,0.0003547128,0.003226563,0.002184592,0.00354805],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01569157,"threshold_uncertainty_score":0.03120047,"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."}}