{"id":"W2989800691","doi":"10.1093/applin/amz051","title":"How Much Knowledge of Derived Words Is Needed for Reading?","year":2019,"lang":"en","type":"article","venue":"Applied Linguistics","topic":"Second Language Acquisition and Learning","field":"Psychology","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Lexis; Prefix; Linguistics; Variety (cybernetics); Word (group theory); Computer science; Reading (process); Lexical analysis; Vocabulary; Root (linguistics); Lexical item; Reading comprehension; Narrative; Natural language processing; Artificial intelligence; Philosophy","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001823255,0.0001479325,0.0002749614,0.0001065072,0.00005170772,0.00002935115,0.0001759813,0.00016243,0.006438858],"category_scores_gemma":[0.0003689507,0.0001516289,0.000087008,0.0001462428,0.00004366932,0.000005499251,0.00003187257,0.0001596285,0.0003345212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001915673,"about_ca_system_score_gemma":0.0000271193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001865451,"about_ca_topic_score_gemma":0.000001158472,"domain_scores_codex":[0.9991176,0.00002290375,0.0002233421,0.0002841032,0.00007333443,0.0002786779],"domain_scores_gemma":[0.9988491,0.0003393138,0.0001640297,0.000399204,0.0001807292,0.00006764528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005287493,0.0003663996,0.0004754519,0.0003427421,0.0003837936,0.000008929639,0.07189067,0.000005617441,0.033065,0.8194236,0.056562,0.01694703],"study_design_scores_gemma":[0.003520295,0.0002227778,0.0004918249,0.00004626867,0.0001213533,0.000005020704,0.05057404,0.0002807517,0.02376108,0.00295654,0.917503,0.0005170329],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.343715,0.00113395,0.005094934,0.0001592189,0.003522246,0.0009272334,0.00005716736,0.0001737663,0.6452165],"genre_scores_gemma":[0.9816254,0.000001387688,0.002983072,0.002173505,0.0009209849,0.00004302129,0.00005412387,0.00004392826,0.0121546],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.860941,"threshold_uncertainty_score":0.9944694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0182697055825244,"score_gpt":0.3103726023983143,"score_spread":0.2921028968157899,"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."}}