{"id":"W4388442618","doi":"10.1002/sta4.630","title":"Mediation analysis with latent factors using simultaneous group‐wise and parameter‐wise penalization","year":2023,"lang":"en","type":"article","venue":"Stat","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Latent variable; Mediation; Latent variable model; Structural equation modeling; Set (abstract data type); Multivariate statistics; Dimension (graph theory); Outcome (game theory); Variable (mathematics); Feature selection; Latent class model; Computer science; Econometrics; Statistics; Data mining; Mathematics; Artificial intelligence; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02706259,0.001963502,0.002417432,0.002229588,0.001535472,0.002142811,0.002827231,0.001608881,0.006564031],"category_scores_gemma":[0.06069167,0.0006890853,0.004143364,0.002762154,0.002400349,0.002249352,0.003766238,0.003362476,0.0006306977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007639149,"about_ca_system_score_gemma":0.002946548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003391727,"about_ca_topic_score_gemma":0.00368175,"domain_scores_codex":[0.9753347,0.02003622,0.0006506622,0.001943148,0.001391423,0.0006439047],"domain_scores_gemma":[0.9497789,0.04137715,0.001872903,0.004655113,0.001960374,0.0003555882],"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":[0.001388589,0.001242067,0.03910437,0.001294503,0.003793396,0.0009694923,0.002631661,0.2543416,0.006780351,0.2414719,0.006192257,0.4407898],"study_design_scores_gemma":[0.0001475459,0.000345003,0.004051068,0.00007875727,0.0003094605,0.0001147259,0.0003140201,0.8799889,0.00182342,0.109755,0.003003951,0.00006805073],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01620703,0.0001302627,0.9823527,0.0001890733,0.0000290584,0.0002408781,0.0001138701,0.0002200217,0.0005172013],"genre_scores_gemma":[0.3451484,0.0001930232,0.6503537,0.0001602588,0.00006896027,0.002113436,0.0006690787,0.0001509551,0.001142281],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02706259,"threshold_uncertainty_score":0.1431223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.176136432841983,"score_gpt":0.4149364984835175,"score_spread":0.2388000656415346,"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."}}