{"id":"W4413805079","doi":"10.1186/s12711-025-00994-y","title":"randPedPCA: rapid approximation of principal components from large pedigrees","year":2025,"lang":"en","type":"article","venue":"Genetics Selection Evolution","topic":"Data Analysis with R","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Biotechnology and Biological Sciences Research Council","keywords":"Pedigree chart; Principal component analysis; Singular value decomposition; Computer science; Matrix (chemical analysis); Inverse; Algorithm; Artificial intelligence; Mathematics; Biology; Genetics; Geometry; Materials science","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":[0.002174406,0.00207805,0.001221739,0.001867187,0.0006873583,0.001681156,0.002057226,0.001123196,0.01118665],"category_scores_gemma":[0.01369373,0.00107158,0.002165399,0.002024239,0.0006669072,0.001506204,0.002106865,0.00229076,0.006851347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007492336,"about_ca_system_score_gemma":0.002604484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02018598,"about_ca_topic_score_gemma":0.02960551,"domain_scores_codex":[0.9987066,0.0005206186,0.0000684925,0.0002603603,0.0003041193,0.0001399503],"domain_scores_gemma":[0.9967688,0.001933097,0.0001614934,0.0005250977,0.0004832921,0.0001282046],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004571894,0.0001539025,0.003757979,0.0005449661,0.0004965562,0.000562033,0.000409695,0.2948537,0.005335802,0.02448539,0.08372898,0.5852137],"study_design_scores_gemma":[0.00008391024,0.00003242682,0.001307267,0.0000601934,0.00004088238,0.0001909547,0.00005718755,0.9570467,0.001535549,0.02502253,0.01458625,0.0000361308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005395731,0.0005437807,0.9811779,0.0002883077,0.000110925,0.0000922681,0.001248874,0.01008292,0.001059336],"genre_scores_gemma":[0.09142208,0.0009132854,0.891965,0.0003299274,0.0001619634,0.0005319685,0.007007088,0.002763711,0.00490492],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02018598,"threshold_uncertainty_score":0.04013699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01134065812263035,"score_gpt":0.241971308950233,"score_spread":0.2306306508276027,"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."}}