{"id":"W4200003768","doi":"10.1177/09622802211061634","title":"Functional response regression model on correlated longitudinal microbiome sequencing data","year":2021,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto; Princess Margaret Cancer Centre; University Health Network","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Princess Margaret Cancer Foundation","keywords":"Functional data analysis; Regression; Regression analysis; Microbiome; Estimator; Computer science; Functional principal component analysis; Functional response; Statistics; Machine learning; Mathematics; Biology; Bioinformatics; Ecology","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":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01609952,0.0001609796,0.0002701933,0.0001696445,0.0001909635,0.00003517013,0.0005136104,0.0004554789,0.001166135],"category_scores_gemma":[0.04446611,0.0001352603,0.00003497814,0.0004833683,0.0005004884,0.000005338429,0.001015412,0.001388813,0.00005696754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001637412,"about_ca_system_score_gemma":0.003454805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003284318,"about_ca_topic_score_gemma":0.00004781799,"domain_scores_codex":[0.9916546,0.005236796,0.0004785591,0.0009857602,0.0009291581,0.0007150743],"domain_scores_gemma":[0.9953448,0.002850925,0.00004136592,0.0009695136,0.0003232883,0.0004701616],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002718569,0.0002055758,0.0001907236,0.00007069191,0.00002535854,0.0007105486,0.00003039748,0.00006657227,0.9087679,0.002052027,0.04503744,0.04012414],"study_design_scores_gemma":[0.01182274,0.003999482,0.07372351,0.003229502,0.00007894428,0.002215907,0.001149375,0.4580984,0.3152989,0.03101956,0.09712071,0.002243044],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1033023,0.0007075263,0.8881899,0.005586271,0.0004040858,0.0003162056,0.0004288189,0.00001635608,0.001048536],"genre_scores_gemma":[0.2057152,0.0006266891,0.78526,0.001459054,0.0003932391,0.00004651855,0.002721308,0.00006558547,0.003712317],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5934691,"threshold_uncertainty_score":0.9997469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3527100488831351,"score_gpt":0.5862111740909964,"score_spread":0.2335011252078613,"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."}}