{"id":"W4282964363","doi":"10.3758/s13421-022-01337-8","title":"Malay Lexicon Project 2: Morphology in Malay word recognition","year":2022,"lang":"en","type":"article","venue":"Memory & Cognition","topic":"Reading and Literacy Development","field":"Psychology","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Agglutinative language; Malay; Morpheme; Prefix; Suffix; Linguistics; Lexicon; Root (linguistics); Word lists by frequency; Mental lexicon; Natural language processing; Computer science; Psychology; Artificial intelligence","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":["insufficient_payload"],"category_scores_codex":[0.0008237656,0.0001943444,0.0002326011,0.000567739,0.000192427,0.00002853525,0.000152375,0.0001002673,0.01172925],"category_scores_gemma":[0.00004741444,0.0002202765,0.00007180763,0.0005475662,0.00004885378,0.0001298123,0.00008361546,0.0004967106,0.001025272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001817015,"about_ca_system_score_gemma":0.00008110744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001620309,"about_ca_topic_score_gemma":0.00002147945,"domain_scores_codex":[0.9976692,0.0006636619,0.0004413396,0.0005514035,0.0002392883,0.0004351029],"domain_scores_gemma":[0.9993771,0.0001248318,0.0001537881,0.0002387044,0.00005775572,0.00004781497],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002500683,0.002337972,0.01275962,0.0001052438,0.0002121398,0.00192378,0.03007191,0.00007116772,0.01719073,0.0004933488,0.054031,0.8783024],"study_design_scores_gemma":[0.02366174,0.003158093,0.6872528,0.000581665,0.0004053635,0.005709226,0.04022373,0.0003899341,0.01511711,0.02448371,0.1946584,0.00435821],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9295269,0.0001047472,0.00006090787,0.0004251521,0.001625784,0.0006492711,0.00007362103,0.0001375132,0.06739613],"genre_scores_gemma":[0.9879504,0.00001017396,0.0004053271,0.001225819,0.0001604266,0.001405898,0.0009729872,0.00003848186,0.007830491],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8739442,"threshold_uncertainty_score":0.9997525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0490910702599647,"score_gpt":0.3170434905285796,"score_spread":0.2679524202686149,"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."}}