{"id":"W4386156211","doi":"10.1080/10705511.2023.2234086","title":"Deep Learning Generalized Structured Component Analysis: An Interpretable Artificial Neural Network Model with Composite Indexes","year":2023,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Component (thermodynamics); Component analysis; Computer science; Artificial intelligence; Function (biology); Flexibility (engineering); Artificial neural network; Independent component analysis; Predictive power; Deep learning; Multivariate statistics; Machine learning; Power (physics); Mathematics; Statistics; Biology","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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0005503002,0.0003964892,0.0004904095,0.000454768,0.002176849,0.0008708662,0.0009559995,0.0001161226,0.00001356807],"category_scores_gemma":[0.00001065851,0.0003088567,0.0002454848,0.002116862,0.00006640731,0.001487367,0.000375359,0.0008659069,0.0000117413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009895103,"about_ca_system_score_gemma":0.00007511095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003183848,"about_ca_topic_score_gemma":0.00009555934,"domain_scores_codex":[0.9966611,0.0003183617,0.0008236169,0.0007140748,0.0007219072,0.0007609996],"domain_scores_gemma":[0.9982774,0.00007649443,0.0004652011,0.0005125781,0.0003175697,0.0003507223],"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":[0.000112297,0.00001295558,0.002044088,0.0000042637,0.0001669818,0.00001602517,0.001399855,0.986621,0.002666584,0.002185128,0.000006143536,0.004764704],"study_design_scores_gemma":[0.0004697628,0.000122582,0.00218657,0.00002230446,0.0001608405,0.0000904849,0.0001594596,0.9680986,0.00002996663,0.02824596,7.401022e-7,0.0004127014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4932423,0.00004217694,0.5058614,0.000317624,0.000157389,0.0001421576,0.00000258352,0.0002311743,0.000003137916],"genre_scores_gemma":[0.8761811,0.00002057081,0.1229978,0.0000425293,0.0003954902,0.00002333367,0.0002825376,0.00003160705,0.00002503063],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3829388,"threshold_uncertainty_score":0.9999363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04006853568200957,"score_gpt":0.2990695866781405,"score_spread":0.2590010509961309,"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."}}