{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002966416,0.00157033,0.001339841,0.002548851,0.001219138,0.003998796,0.002407937,0.003843447,0.03267006],"category_scores_gemma":[0.01899896,0.0007748544,0.00121348,0.001605636,0.001882005,0.005745159,0.002444034,0.003516067,0.01157702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001001637,"about_ca_system_score_gemma":0.001294871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001601223,"about_ca_topic_score_gemma":0.002100519,"domain_scores_codex":[0.9962824,0.001015709,0.0004917406,0.0006383492,0.001380139,0.000191658],"domain_scores_gemma":[0.9878619,0.006848107,0.0004791453,0.001056799,0.003175726,0.0005783292],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002386318,0.0001613648,0.00214328,0.0008218407,0.00008965262,0.001454399,0.0005541823,0.002047213,0.002312846,0.03462695,0.4220161,0.5335335],"study_design_scores_gemma":[0.00003151593,0.0002482788,0.003929511,0.001043039,0.0001177578,0.007694265,0.0006997164,0.01581321,0.00477283,0.1011184,0.8643519,0.0001796423],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.007502241,0.1049026,0.5983243,0.04276469,0.1618142,0.0005122882,0.0009287902,0.006684395,0.0765665],"genre_scores_gemma":[0.09967429,0.08978412,0.3099337,0.01376176,0.1918571,0.0004710003,0.003698674,0.002843632,0.2879757],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.03267006,"threshold_uncertainty_score":0.1092922,"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."}}