{"id":"W4416183343","doi":"10.1109/mipr67560.2025.00051","title":"A Discriminant Correlation Neural Network for Feature Representation Learning","year":2025,"lang":"","type":"article","venue":"","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Feature learning; Feature (linguistics); Artificial neural network; Key (lock); Representation (politics); Deep learning; Linear discriminant analysis; Feature extraction; Convolutional neural network","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":[0.000398373,0.0002460231,0.0002998723,0.0001793757,0.000573315,0.0001122901,0.0001029251,0.0004468128,0.0008295523],"category_scores_gemma":[0.0002430396,0.0002387266,0.0002628506,0.0005494873,0.00006257066,0.0001487009,0.00004356987,0.0005355653,0.000120661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005946475,"about_ca_system_score_gemma":0.00004992439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008788776,"about_ca_topic_score_gemma":0.0001294165,"domain_scores_codex":[0.9978578,0.0004476834,0.0004731685,0.0006199394,0.0001273233,0.0004740935],"domain_scores_gemma":[0.998787,0.0004286659,0.0002631655,0.0002517855,0.0001945054,0.00007483682],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001379274,0.0003722343,0.02380145,0.0002043292,0.0003149721,0.000005773909,0.002382849,0.0181527,0.0003394252,0.08853139,0.4004027,0.4641129],"study_design_scores_gemma":[0.009151007,0.001204898,0.3259289,0.0006398143,0.001346361,0.00003625946,0.009092147,0.4282173,0.0003279971,0.01848906,0.2046421,0.0009241268],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02349978,0.002085448,0.6062723,0.01757538,0.02579967,0.003674158,0.00003601719,0.0003480676,0.3207091],"genre_scores_gemma":[0.747616,0.0001166271,0.001731543,0.001200992,0.0007472286,0.0001307401,0.0009167921,0.00002728122,0.2475128],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7241162,"threshold_uncertainty_score":0.9734988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04018735590926942,"score_gpt":0.3633648150830523,"score_spread":0.3231774591737829,"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."}}