{"id":"W4416191036","doi":"10.1093/biomet/asaf081","title":"Thinning a Wishart random matrix","year":2025,"lang":"en","type":"article","venue":"Biometrika","topic":"Random Matrices and Applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wishart distribution; Independent and identically distributed random variables; Sample mean and sample covariance; Random matrix; Covariance matrix; Gaussian; Data Matrix; Matrix (chemical analysis); Sample (material)","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":[],"consensus_categories":[],"category_scores_codex":[0.0004247437,0.0001046775,0.0002262154,0.0006406155,0.0001350502,0.00007812204,0.0002232125,0.00006937022,0.0001600146],"category_scores_gemma":[0.0004777283,0.00008700732,0.0001134253,0.002795159,0.00002926316,0.00006169386,0.0000640462,0.00008182527,0.000125168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003044961,"about_ca_system_score_gemma":0.00003530534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001632649,"about_ca_topic_score_gemma":0.000001760615,"domain_scores_codex":[0.9991764,0.00002587788,0.0002750082,0.0001864522,0.0001542739,0.000181987],"domain_scores_gemma":[0.9987639,0.0007232971,0.00008401376,0.0003269569,0.00005841678,0.00004340271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002502622,0.000460658,0.001948582,0.0004480603,0.0002881464,0.000007619842,0.0003592951,0.000003932561,0.00681973,0.5111154,0.4490556,0.0292427],"study_design_scores_gemma":[0.008746685,0.00003450622,0.001006788,0.0001226384,0.0002014804,0.000007042775,0.0002010888,0.0008029369,0.004944194,0.2382775,0.745297,0.0003581851],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4279862,0.00951453,0.3077955,0.008851559,0.001399805,0.003337342,0.0001421465,0.001443141,0.2395298],"genre_scores_gemma":[0.9282979,0.0001436542,0.04062728,0.0003042503,0.0001976545,0.0001774962,0.0000176611,0.00003024648,0.03020381],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5003117,"threshold_uncertainty_score":0.3548056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03428334743050858,"score_gpt":0.3860407606966231,"score_spread":0.3517574132661145,"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."}}