{"id":"W2222111835","doi":"","title":"The Application of Corpus in English Writing and Its Influences","year":2016,"lang":"en","type":"article","venue":"Studies in sociology of science","topic":"Second Language Acquisition and Learning","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Corpus linguistics; Computer science; Section (typography); Natural language processing; Strengths and weaknesses; Artificial intelligence; Text corpus; Linguistics; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001437963,0.00003492969,0.0001082881,0.00006532801,0.000105051,8.219015e-7,0.0001577042,0.00003276338,0.00003080372],"category_scores_gemma":[0.001087845,0.00002020505,0.000008319644,0.000171089,0.01279419,0.00005750027,0.00006739136,0.00006197908,0.000001861002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001599211,"about_ca_system_score_gemma":0.00001422321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001355853,"about_ca_topic_score_gemma":0.00001268417,"domain_scores_codex":[0.9993781,0.00008881577,0.0001901186,0.0001453565,0.00005833353,0.0001392536],"domain_scores_gemma":[0.9987147,0.0009699741,0.0001178325,0.00008061916,0.0001077974,0.00000904125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.00003585387,0.00003134141,0.276953,0.00002298535,0.00002259805,0.000003427306,0.5333282,0.000005473742,0.03228988,0.1052046,0.00003966787,0.05206298],"study_design_scores_gemma":[0.0004549772,0.00007199025,0.5092098,0.00005006352,0.000002042821,0.000002699265,0.4864171,0.00001027075,0.00074092,0.002733392,0.0002510128,0.00005576805],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9781476,0.02017334,0.000005544705,0.0002404502,0.0001685927,0.00006210861,7.314549e-7,0.000005199696,0.001196419],"genre_scores_gemma":[0.9994691,0.0002739781,0.00002196445,0.0001639162,0.00002188079,0.00001914423,4.456392e-8,0.000001130548,0.00002882127],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2322568,"threshold_uncertainty_score":0.9898924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03959051415692574,"score_gpt":0.4022037840373577,"score_spread":0.362613269880432,"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."}}