{"id":"W2152175132","doi":"10.11139/cj.28.2.460-472","title":"Retention in SLA Lexical Processing","year":2011,"lang":"en","type":"article","venue":"CALICO Journal","topic":"Second Language Acquisition and Learning","field":"Psychology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Victoria","funders":"","keywords":"Computer science; Natural language processing; Linguistics; Artificial intelligence; Psychology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001623189,0.0002235549,0.0004735213,0.0004620119,0.0002360809,0.000928395,0.0004730136,0.000441328,0.003544977],"category_scores_gemma":[0.009318219,0.0001457584,0.0002030038,0.0002392277,0.0005351757,0.0009987311,0.0009418897,0.0007269491,0.0006775289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000354614,"about_ca_system_score_gemma":0.0005705566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00112374,"about_ca_topic_score_gemma":0.0006359026,"domain_scores_codex":[0.9990902,0.0001377584,0.00009096949,0.0002009967,0.0002765485,0.0002035135],"domain_scores_gemma":[0.9943147,0.00209903,0.0009328208,0.0009898314,0.001050791,0.0006127772],"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":[0.01694437,0.006138985,0.1573509,0.000556713,0.0002333708,0.0007963381,0.007779448,0.0006973874,0.5136217,0.001066583,0.001122916,0.2936914],"study_design_scores_gemma":[0.0004959502,0.07254294,0.6513933,0.0001463252,0.0003059374,0.002120346,0.003424265,0.00221731,0.2593331,0.002008696,0.005902001,0.0001098348],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992256,0.0001574996,0.0001144813,0.00001281832,0.000005365406,0.000006364545,0.00002528004,0.00001432487,0.0004381732],"genre_scores_gemma":[0.99873,0.00005307452,0.00008481082,0.00002025439,0.000004596957,0.000008019201,0.00006475047,0.000007126795,0.001027334],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003544977,"threshold_uncertainty_score":0.01185918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09312754495153247,"score_gpt":0.3374444740000678,"score_spread":0.2443169290485354,"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."}}