{"id":"W2767253414","doi":"10.3758/s13428-017-0981-8","title":"MorphoLex: A derivational morphological database for 70,000 English words","year":2017,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Reading and Literacy Development","field":"Psychology","cited_by":92,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University; Centres Intégré Universitaires de Santé et de Services Sociaux","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Suffix; Computer science; Natural language processing; Lexicon; Word (group theory); Root (linguistics); Artificial intelligence; Lexical decision task; Noun; Linguistics; Cognition; Psychology","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":[0.01139074,0.0002254692,0.0003364877,0.0003163584,0.001225723,0.0004395732,0.001293981,0.0002337962,0.003918114],"category_scores_gemma":[0.00557165,0.0001957377,0.0001445055,0.0001897252,0.0005372837,0.0002684227,0.0004829601,0.0007805303,0.0001395065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001248196,"about_ca_system_score_gemma":0.0001389822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002907237,"about_ca_topic_score_gemma":0.00000404398,"domain_scores_codex":[0.9955681,0.001465368,0.0004460692,0.0008396989,0.0006734388,0.001007311],"domain_scores_gemma":[0.9953814,0.001719434,0.0001484567,0.001724227,0.0006901525,0.0003363026],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009786937,0.00197592,0.0918581,0.00006377554,0.0001467039,0.0009399522,0.008393152,0.000001014143,0.04384521,0.03254629,0.1286851,0.6905661],"study_design_scores_gemma":[0.002175023,0.0003260967,0.4704172,0.00006643568,0.00003792096,0.00009410323,0.001048003,0.00003799785,0.006151546,0.001478353,0.5176719,0.0004954339],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9223922,0.0002785113,0.04355606,0.0008999029,0.003061233,0.002282411,0.0005519212,0.000197772,0.02677993],"genre_scores_gemma":[0.33219,0.00002090679,0.6294411,0.0001600676,0.001018579,0.006005033,0.0003602867,0.00008166683,0.03072238],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6900706,"threshold_uncertainty_score":0.9969925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4441928567752986,"score_gpt":0.6216715043181028,"score_spread":0.1774786475428042,"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."}}