{"id":"W3217573406","doi":"10.82308/37322","title":"Penalized regression methods for interaction and mixed-effects models with applications to genomic and brain imaging data","year":2019,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ludmer Centre for Neuroinformatics and Mental Health","keywords":"Neuroimaging; Computer science; Regression analysis; Regression; Artificial intelligence; Computational biology; Machine learning; Psychology; Biology; Neuroscience; Statistics; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001864504,0.0002830154,0.0004439745,0.0001293236,0.0003619455,0.00009693613,0.000323352,0.00008747666,0.00002332355],"category_scores_gemma":[0.002110468,0.0002253552,0.00003456292,0.0001711917,0.00005054957,0.0005915742,0.0004644685,0.0002962313,0.00001133539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009501241,"about_ca_system_score_gemma":0.00001303642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003383362,"about_ca_topic_score_gemma":0.0000153832,"domain_scores_codex":[0.9978393,0.0004840005,0.0003667235,0.0008221696,0.0001636795,0.0003240788],"domain_scores_gemma":[0.9931375,0.005436724,0.000192645,0.0008481393,0.0001412366,0.0002437338],"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":[0.0001134916,0.00003680359,0.00002258038,0.0002580129,0.00002942255,9.610551e-7,0.000005990551,0.000004041174,0.06300188,0.4617655,0.000008708098,0.4747526],"study_design_scores_gemma":[0.001502475,0.0002124661,0.0003113094,0.0004332492,0.0001565154,0.00005818497,0.0001104673,0.0203955,0.01700997,0.9392597,0.02003401,0.0005161717],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4304045,0.0002329249,0.5584005,0.00023546,0.0002487555,0.004227163,0.000884674,0.0001928463,0.005173214],"genre_scores_gemma":[0.2016651,0.00002099647,0.7974815,0.0002995296,0.00001434411,0.0002718583,0.00003003287,0.00005820389,0.0001584386],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4774942,"threshold_uncertainty_score":0.9189718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0950309754310739,"score_gpt":0.4078149162326107,"score_spread":0.3127839408015368,"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."}}