{"id":"W7110836110","doi":"10.1080/10705511.2025.2588572","title":"Evaluating Approaches for the Handling of Sign Reflection in Bayesian Latent Variable Models","year":2025,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reflection (computer programming); Latent variable; Bayesian probability; Variable (mathematics); Sign (mathematics); Pattern recognition (psychology)","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":[0.002626127,0.0001850563,0.0002558069,0.0003284471,0.0007525821,0.0001942041,0.0006628822,0.0001020949,0.000002131023],"category_scores_gemma":[0.0002107264,0.0001362091,0.0001257207,0.0005245907,0.000027092,0.000929088,0.0002147945,0.0004039842,1.551548e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002118498,"about_ca_system_score_gemma":0.0003450979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006256998,"about_ca_topic_score_gemma":0.00001499716,"domain_scores_codex":[0.9977662,0.0001887575,0.0009036465,0.0003952672,0.0004082247,0.0003378976],"domain_scores_gemma":[0.9985276,0.000387153,0.0003325062,0.0003750787,0.0003290402,0.00004856549],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005632058,0.00001124553,0.000162043,0.00003542929,0.0000243398,3.635436e-7,0.001954974,0.9316579,0.001419755,0.02831534,6.772391e-7,0.03636167],"study_design_scores_gemma":[0.0006464617,0.0000483995,0.00005863309,0.0001389224,0.00002028572,0.00001201132,0.0003199312,0.7080122,0.0001378939,0.2905074,1.360486e-7,0.0000977871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1323391,0.0003998429,0.8653643,0.0007668919,0.0005842802,0.0004553239,0.000001343778,0.00003367107,0.00005527383],"genre_scores_gemma":[0.6390043,0.00001100605,0.3608392,0.00001057721,0.00007288397,0.00002865784,0.000002240138,0.00000717206,0.00002390555],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5066652,"threshold_uncertainty_score":0.5788329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2054349573084018,"score_gpt":0.3748256179263479,"score_spread":0.1693906606179461,"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."}}