{"id":"W4407126964","doi":"10.1080/23273798.2025.2457976","title":"Disentangling semantic prediction and association in processing filler-gap dependencies: an MEG study in English","year":2025,"lang":"en","type":"article","venue":"Language Cognition and Neuroscience","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"York University; New York University Abu Dhabi","keywords":"Association (psychology); Natural language processing; Filler (materials); Computer science; Artificial intelligence; Speech recognition; Psychology; Materials science; Composite material","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022288,0.0001504534,0.0001619306,0.0001391523,0.0001661466,0.0002900815,0.0001447997,0.000319803,0.0008559349],"category_scores_gemma":[0.0008293916,0.0001636753,0.0001536915,0.00009683133,0.0004794453,0.0003468346,0.0002178277,0.0002730898,0.0001467044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001121812,"about_ca_system_score_gemma":0.0001216622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001466937,"about_ca_topic_score_gemma":0.002564995,"domain_scores_codex":[0.9999439,0.0000135725,0.000004986637,0.0000172454,0.000008269006,0.00001214498],"domain_scores_gemma":[0.999729,0.0001817797,0.00002783936,0.0000141715,0.00002161381,0.00002559704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001513092,0.000270841,0.02843622,0.0001325337,0.00004005143,0.002058735,0.002657143,0.000126785,0.9542914,0.0004111448,0.000133944,0.009928028],"study_design_scores_gemma":[0.0001731736,0.0008405313,0.9342294,0.00002082612,0.000110839,0.002984212,0.002260067,0.002469594,0.05430073,0.001384487,0.001200233,0.00002588814],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989881,0.00004885539,0.000425874,0.00003372763,0.000002544986,0.000006426272,0.000037916,0.000002945081,0.0004537343],"genre_scores_gemma":[0.9989107,0.00005602092,0.0005767436,0.00003862221,0.00001026977,0.00001095831,0.00006869734,0.000008250007,0.0003198718],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001466937,"threshold_uncertainty_score":0.002916753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02825064807045886,"score_gpt":0.3121717440090161,"score_spread":0.2839210959385572,"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."}}