{"id":"W4408197111","doi":"10.1016/j.neuroimage.2026.122030","title":"Common but different: An ERP study of single- and multi-source interference processing in MSIT","year":2025,"lang":"en","type":"preprint","venue":"NeuroImage","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre","funders":"Narodowe Centrum Nauki; Fundacja na rzecz Nauki Polskiej","keywords":"Interference (communication); Computer science; Telecommunications","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"],"consensus_categories":[],"category_scores_codex":[0.0002857659,0.0003198718,0.0005282062,0.0004514875,0.00006225288,0.0004153253,0.001419737,0.0001590766,0.000001272523],"category_scores_gemma":[0.0000610674,0.0003190314,0.00004176734,0.0002625102,0.00007459315,0.000365772,0.002929014,0.0008384655,4.466065e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003996481,"about_ca_system_score_gemma":0.00006618323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003122003,"about_ca_topic_score_gemma":0.0006254013,"domain_scores_codex":[0.9975299,0.0005301164,0.0005569091,0.0009109178,0.0002632766,0.0002088358],"domain_scores_gemma":[0.998355,0.00008634961,0.000335238,0.001049435,0.0001059223,0.00006802916],"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":[0.0001835384,0.01869187,0.1427848,0.002353699,0.0000529317,0.0002025161,0.1399043,0.00170897,0.06230268,0.001017285,0.00008009373,0.6307173],"study_design_scores_gemma":[0.002327695,0.002170924,0.3039164,0.001677404,0.00005625999,0.00001892222,0.001228167,0.6596304,0.02562424,0.002044284,0.00007806933,0.001227237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7712358,0.00006698064,0.227277,0.00008780947,0.00008250267,0.0006235081,0.000006145649,0.0002803298,0.0003399686],"genre_scores_gemma":[0.9899608,0.00001045206,0.009617004,0.0001680555,0.00001114303,0.00004454832,0.000005223344,0.00001558489,0.0001672151],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6579214,"threshold_uncertainty_score":0.9999261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06581982928564543,"score_gpt":0.3280092017217521,"score_spread":0.2621893724361067,"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."}}