{"id":"W2008178804","doi":"10.1016/j.brainres.2010.12.022","title":"Interplay between morphology and frequency in lexical access: The case of the base frequency effect","year":2010,"lang":"en","type":"article","venue":"Brain Research","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","cited_by":43,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"National Institute on Deafness and Other Communication Disorders; James S. McDonnell Foundation; National Institutes of Health; Canada Research Chairs; National Institute of Mental Health; David and Lucile Packard Foundation","keywords":"Word lists by frequency; Suffix; Frequency; Lexical decision task; Word (group theory); Lexical access; Base (topology); Computer science; Variety (cybernetics); Psychology; Natural language processing; Communication; Artificial intelligence; Speech recognition; Linguistics; Mathematics; Cognition; Neuroscience; Statistics; Sentence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001235729,0.0002699364,0.0006430053,0.0008795296,0.0004412647,0.002763909,0.0006303849,0.001332634,0.005277748],"category_scores_gemma":[0.01077518,0.0004636154,0.000258122,0.0007655104,0.002565405,0.004463165,0.001122119,0.001129279,0.0005868524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002315751,"about_ca_system_score_gemma":0.0003312746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007366718,"about_ca_topic_score_gemma":0.0007166018,"domain_scores_codex":[0.9995246,0.0001342694,0.00002569528,0.0001196272,0.0001351558,0.00006071043],"domain_scores_gemma":[0.995729,0.002804874,0.0002879306,0.0007205817,0.0002735617,0.0001839633],"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":[0.001562355,0.0002820167,0.02872889,0.0004118184,0.0002348393,0.003867162,0.003856921,0.004248073,0.4613259,0.34214,0.002354122,0.150988],"study_design_scores_gemma":[0.0004069801,0.0004457957,0.1425676,0.00008674715,0.0003086928,0.006474173,0.001502105,0.02250277,0.03902019,0.7769591,0.009449455,0.0002764541],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9208727,0.001666029,0.03285572,0.00243032,0.0001328038,0.00002575355,0.0001986427,0.0001957063,0.04162228],"genre_scores_gemma":[0.9931623,0.000365929,0.004854466,0.0001688751,0.0001236575,0.00001145858,0.00003472251,0.00006543724,0.001213198],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005277748,"threshold_uncertainty_score":0.01765585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08429857527796623,"score_gpt":0.4514177299305372,"score_spread":0.367119154652571,"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."}}