{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003794804,0.00005923733,0.00008517588,0.0001251851,0.00007571434,0.00003417066,0.00008614481,0.00008083235,0.09777354],"category_scores_gemma":[0.00003231097,0.00005167158,0.00004888414,0.0001111577,0.0000283676,0.0001065497,0.000008755585,0.0004372596,0.0002657331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002603391,"about_ca_system_score_gemma":0.0000183907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001950998,"about_ca_topic_score_gemma":0.00000172095,"domain_scores_codex":[0.9992701,0.0001398873,0.0002056743,0.0001075841,0.00008176474,0.0001949456],"domain_scores_gemma":[0.999717,0.00001024964,0.0000828577,0.00007636986,0.00002970855,0.00008379896],"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":[0.0006718927,0.0008156898,0.03884408,0.000040172,0.00008581633,0.006006791,0.1580924,0.000007109275,0.006157715,0.01575262,0.020923,0.7526027],"study_design_scores_gemma":[0.004690992,0.000420579,0.8948118,0.000279823,0.00004708578,0.0101939,0.04150126,0.0003594747,0.0005768213,0.00431317,0.0421808,0.0006242995],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8786296,0.003879829,0.002683772,0.0001230415,0.0006347945,0.0000428265,4.413648e-7,0.00003468278,0.113971],"genre_scores_gemma":[0.9947941,0.000002124253,0.0003435162,0.002970215,0.0003139451,0.000002794416,0.000001681471,0.00001062834,0.001560991],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8559677,"threshold_uncertainty_score":0.9030512,"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."}}