{"id":"W4391841969","doi":"10.1177/17470218241234668","title":"Malay Lexicon Project 3: The impact of orthographic–semantic consistency on lexical decision latencies","year":2024,"lang":"en","type":"article","venue":"Quarterly Journal of Experimental Psychology","topic":"Second Language Acquisition and Learning","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lexicon; Malay; Lexical decision task; Consistency (knowledge bases); Natural language processing; Computer science; Artificial intelligence; Linguistics; Orthographic projection; Psychology; 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":[0.0006848027,0.0002322774,0.0004037813,0.0005796264,0.00008827179,0.00005962167,0.0003393431,0.0001733196,0.01445616],"category_scores_gemma":[0.00002306474,0.0001354213,0.0006467921,0.0003858447,0.0003288912,0.0001374231,0.00001150884,0.0006103904,0.0001502522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003837756,"about_ca_system_score_gemma":0.000077651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005463234,"about_ca_topic_score_gemma":0.000001634518,"domain_scores_codex":[0.9978489,0.0004638409,0.0008079083,0.0002955617,0.000265578,0.0003181839],"domain_scores_gemma":[0.9986843,0.0004487959,0.0003218233,0.0003775809,0.00007865965,0.00008877215],"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":[0.01172773,0.00567182,0.007586578,0.00007081724,0.00418735,0.006701178,0.1592932,0.00004431436,0.3990927,0.0538105,0.09346707,0.2583467],"study_design_scores_gemma":[0.0174598,0.1787018,0.5123398,0.002403419,0.0005457212,0.0492476,0.2072398,0.0003950665,0.008968669,0.01018046,0.01034282,0.002175037],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9643038,0.02257552,0.0002903577,0.0003051375,0.001930928,0.0001845008,0.0000094488,0.00003013498,0.01037021],"genre_scores_gemma":[0.9985508,0.0000149374,0.0001348918,0.0008319819,0.0002892501,0.000008937744,0.000002699624,0.00002983056,0.0001366201],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5047532,"threshold_uncertainty_score":0.9864448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03845392388830106,"score_gpt":0.4352862171156964,"score_spread":0.3968322932273954,"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."}}