{"id":"W4416371877","doi":"10.2316/j.2026.206-1214","title":"FUZZY LOGIC-BASED ERROR DETECTION AND CORRECTION IN ENGLISH WRITING FOR LANGUAGE LEARNING. 85-97","year":2025,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Fuzzy logic; Error detection and correction; Natural language; Fuzzy control system; Error analysis","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":[],"consensus_categories":[],"category_scores_codex":[0.0003976357,0.00005851402,0.00008654449,0.0003459775,0.00004779291,0.0001931608,0.000150871,0.00005126067,3.742738e-7],"category_scores_gemma":[0.0005192643,0.00005385796,0.0000260253,0.0001254633,0.0000166221,0.0003875644,0.00003791654,0.0001528465,8.501301e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007023362,"about_ca_system_score_gemma":0.00003514413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009533989,"about_ca_topic_score_gemma":0.00001224325,"domain_scores_codex":[0.9994243,0.00003178592,0.0002439874,0.00009514618,0.0001409448,0.00006386881],"domain_scores_gemma":[0.9991873,0.0001323925,0.0002281379,0.0000353514,0.0004002242,0.00001657858],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000070899,0.00007398767,0.003267055,0.00006886981,0.00003552016,0.00002080196,0.001479113,0.02918825,0.01072653,0.01808849,0.00006976152,0.9369107],"study_design_scores_gemma":[0.0007109263,0.0001308701,0.003612254,0.000380002,0.000009963287,0.00003425993,0.0002142008,0.9688017,0.01105667,0.01486606,0.00009172803,0.00009140267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03434803,0.0006957388,0.962949,0.0009160503,0.000856238,0.00007484588,4.453169e-7,0.00007885987,0.00008073337],"genre_scores_gemma":[0.8556343,0.00001603584,0.1441254,0.0001130867,0.00007182125,0.000002477767,0.000001669767,0.000002581328,0.00003254417],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9396134,"threshold_uncertainty_score":0.2196264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009513769704676007,"score_gpt":0.2914994519304855,"score_spread":0.2819856822258095,"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."}}