{"id":"W1832439498","doi":"10.1558/cj.v20i3.433-436","title":"Error Diagnosis and Error Correction in CALL","year":2003,"lang":"en","type":"article","venue":"CALICO Journal","topic":"Educational Technology and Assessment","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Simon Fraser University","funders":"","keywords":"Computer science; Error detection and correction; Error analysis; Speech recognition; Natural language processing; Artificial intelligence; Algorithm; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0003107653,0.0000537444,0.00006705985,0.0001095795,0.0001053288,0.00005776893,0.0001539631,0.00006043924,0.00002784835],"category_scores_gemma":[0.0001005855,0.00004771068,0.00001624873,0.0001724983,0.00003051636,0.0001955069,0.00002302583,0.0002986868,0.000008431081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006129102,"about_ca_system_score_gemma":0.00009157684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006001198,"about_ca_topic_score_gemma":0.00001614599,"domain_scores_codex":[0.9994648,0.00006926233,0.00013018,0.000117391,0.00008790898,0.0001304249],"domain_scores_gemma":[0.9996949,0.00006426102,0.00005160501,0.00009574217,0.00002982341,0.00006360025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000004668425,0.0005469759,0.7192055,0.000008895424,0.00002651537,0.0001160996,0.001469727,0.000173725,0.0003456398,0.1528396,0.05660957,0.06865311],"study_design_scores_gemma":[0.001761891,0.0004180953,0.6279922,0.0001931932,0.00001994311,0.005481569,0.001411618,0.01155475,0.007107078,0.0658206,0.2775509,0.0006881318],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9238138,0.0007475734,0.06117904,0.008943099,0.003191821,0.0000927304,5.246503e-7,0.00005088394,0.001980515],"genre_scores_gemma":[0.9928361,0.00009397671,0.006352589,0.0003238136,0.00003887887,0.00001460898,2.133811e-7,0.000002637472,0.0003372034],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2209414,"threshold_uncertainty_score":0.1945585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03002669766090879,"score_gpt":0.3169435381599876,"score_spread":0.2869168404990788,"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."}}