{"id":"W4414296644","doi":"10.1080/00273171.2025.2551373","title":"Regularized Cross-Sectional Network Modeling with Missing Data: A Comparison of Methods","year":2025,"lang":"en","type":"article","venue":"Multivariate Behavioral Research","topic":"Mental Health Research Topics","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of the Fraser Valley; McGill University","funders":"","keywords":"Missing data; Covariance; Graphical model; Covariance matrix; Estimation of covariance matrices; Lasso (programming language); Gaussian; Data modeling; Sample (material)","routes":{"ca_aff":true,"ca_fund":false,"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":[0.01046984,0.0002137949,0.0005198793,0.0004610118,0.0007258664,0.0001712761,0.001240838,0.0003222944,0.0006527878],"category_scores_gemma":[0.0003732365,0.0001861285,0.00006366887,0.001392518,0.0005031322,0.0002320112,0.001066618,0.001677796,0.00003383669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003293116,"about_ca_system_score_gemma":0.0006622556,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00843833,"about_ca_topic_score_gemma":0.0001764127,"domain_scores_codex":[0.9928252,0.00293655,0.000901155,0.0009601064,0.001085449,0.001291504],"domain_scores_gemma":[0.9960702,0.001036163,0.0001374784,0.001669302,0.0008170362,0.0002698357],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.007681,0.003181502,0.7743167,0.0004041183,0.0002428039,0.0000567815,0.001689463,0.003363982,0.01157703,0.02014773,0.001928987,0.17541],"study_design_scores_gemma":[0.006516837,0.001066359,0.4372038,0.0004848646,0.00006160709,0.00001303006,0.001287867,0.5441068,0.001229613,0.003227038,0.004344304,0.0004578712],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7286583,0.0009945383,0.2607148,0.0003588436,0.000935218,0.001798109,0.00008761418,0.0000988831,0.006353734],"genre_scores_gemma":[0.8268445,0.000006325125,0.1685649,0.00001920803,0.0001679919,0.0001482002,0.0001556516,0.00004052799,0.004052814],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5407429,"threshold_uncertainty_score":0.9981645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7826204345838472,"score_gpt":0.7286777603513154,"score_spread":0.05394267423253185,"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."}}