{"id":"W2942481792","doi":"10.1111/biom.13384","title":"Poisson PCA: Poisson measurement error corrected PCA, with application to microbiome data","year":2020,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Poisson distribution; Outlier; Principal component analysis; Poisson regression; Parametric statistics; Variance (accounting); Transformation (genetics); Latent variable","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":[0.0008277507,0.0002283477,0.0003553224,0.0004492902,0.00008079733,0.00007724047,0.0007182813,0.0001021548,0.00008875313],"category_scores_gemma":[0.00803469,0.0001836882,0.00002854453,0.005993161,0.00004383333,0.00007861378,0.0002951059,0.0001495823,0.00018766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001365566,"about_ca_system_score_gemma":0.000083327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008479201,"about_ca_topic_score_gemma":0.0000207789,"domain_scores_codex":[0.9978127,0.00009769928,0.0003993302,0.0006339639,0.0007178605,0.0003384677],"domain_scores_gemma":[0.9976397,0.000505635,0.0001818665,0.0008557024,0.0004577417,0.0003593304],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005276285,0.001154165,0.001915605,0.0009041135,0.0002801675,0.00002876654,0.00122587,0.00000490079,0.3002149,0.02621844,0.2009098,0.4666157],"study_design_scores_gemma":[0.007915868,0.00787044,0.05039444,0.0008216301,0.00157724,0.00008231499,0.001521855,0.05703048,0.1219803,0.02924539,0.715848,0.005712067],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006432941,0.0001057197,0.988953,0.002374452,0.0001474691,0.0007321502,0.0005680495,0.0001596482,0.0005265952],"genre_scores_gemma":[0.2880211,0.000009592984,0.7106054,0.0009329284,0.000144373,0.00004099863,0.000121363,0.00005123267,0.0000730094],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5149382,"threshold_uncertainty_score":0.9618856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3398189776315755,"score_gpt":0.3932750149486975,"score_spread":0.05345603731712195,"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."}}