{"id":"W4292997377","doi":"10.1177/00238309221111752","title":"Computational Modeling of an Auditory Lexical Decision Experiment Using DIANA","year":2022,"lang":"en","type":"article","venue":"Language and Speech","topic":"Phonetics and Phonology Research","field":"Psychology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Lexical decision task; Pseudoword; Lexicon; Computer science; Speech recognition; Word recognition; Latency (audio); Word (group theory); Natural language processing; Artificial intelligence; Psychology; Linguistics; Cognition","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001938352,0.00004771374,0.00009349301,0.00006992077,0.000110654,0.000006758843,0.00009229491,0.0000341948,0.001479834],"category_scores_gemma":[0.000006231548,0.0000475683,0.00002114093,0.00006513308,0.00003412175,0.00001902299,0.0001014746,0.0001396869,0.000004522343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001718159,"about_ca_system_score_gemma":0.00002207339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005391494,"about_ca_topic_score_gemma":0.00001048703,"domain_scores_codex":[0.9993529,0.00008933734,0.0001147284,0.0001531413,0.0001651056,0.0001247585],"domain_scores_gemma":[0.9997268,0.00005571133,0.00002160598,0.0001299874,0.00001662834,0.00004921878],"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":[0.001983292,0.002935617,0.003774752,0.00005780235,0.0003151447,0.002206676,0.2600578,0.2209569,0.1926616,0.005046383,0.003257456,0.3067465],"study_design_scores_gemma":[0.001672657,0.0006385135,0.005995841,0.00001114596,0.00001753473,0.0002592239,0.02122897,0.961489,0.001811379,0.006274063,0.0003410744,0.0002606057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958526,0.0008500445,0.002353091,0.00002219299,0.0002290391,0.00006261845,0.00001830301,0.000008443999,0.0006036721],"genre_scores_gemma":[0.9960768,0.000002040808,0.003628126,0.00006243109,0.00009925224,0.000006839811,0.00001849899,0.00000735843,0.00009861436],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.740532,"threshold_uncertainty_score":0.9994329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0460913719733166,"score_gpt":0.3910215542915912,"score_spread":0.3449301823182747,"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."}}