{"id":"W2909019525","doi":"10.3968/9208","title":"Intra-Lingual and Inter-Lingual Errors in Chinese College Freshmen’s English Writing","year":2016,"lang":"en","type":"article","venue":"Studies in literature and language","topic":"Second Language Acquisition and Learning","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sentence; Syntax; Error analysis; Linguistics; Psychology; College English; China; Computer science; Mathematics education; Natural language processing; History; Mathematics; Philosophy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003299177,0.0003338677,0.000356007,0.003656294,0.0005689893,0.0007404735,0.0003659007,0.0003167025,0.001344538],"category_scores_gemma":[0.01867104,0.0001416903,0.0003979319,0.002363983,0.001111116,0.0007661004,0.0009087932,0.0003874601,0.0002544112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000462945,"about_ca_system_score_gemma":0.0007246935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006088573,"about_ca_topic_score_gemma":0.009312502,"domain_scores_codex":[0.9951347,0.000787173,0.001164973,0.0007688385,0.001798344,0.0003461451],"domain_scores_gemma":[0.9710934,0.01384443,0.007402618,0.00214794,0.004649365,0.0008622186],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002372662,0.00004572674,0.9692991,0.0000631583,0.00006694042,0.000371087,0.004517984,0.0001261708,0.001634586,0.00007465344,0.00009728494,0.02346601],"study_design_scores_gemma":[0.00000343214,0.0000604339,0.9946342,0.00001218699,0.00004055302,0.0002614598,0.002773888,0.0003782577,0.001475602,0.00006853775,0.0002798807,0.00001160383],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989679,0.0000898923,0.000355097,0.00001683021,0.000008085471,0.00001026526,0.00007389003,0.000006310238,0.0004718557],"genre_scores_gemma":[0.9990676,0.00004542088,0.0003069039,0.000007940735,0.000008716676,0.00001114008,0.0001233557,0.000006362871,0.00042259],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006088573,"threshold_uncertainty_score":0.01744795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01139067901797199,"score_gpt":0.3410987157581233,"score_spread":0.3297080367401513,"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."}}