{"id":"W2809529804","doi":"10.5539/ijel.v8n5p259","title":"The Human Intelligence vs. Artificial Intelligence: Issues and Challenges in Computer Assisted Language Learning","year":2018,"lang":"en","type":"article","venue":"International Journal of English Linguistics","topic":"AI in Service Interactions","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Human intelligence; Context (archaeology); Ambiguity; Software; Natural language processing; Programming language","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":[0.03587303,0.0006773204,0.0016214,0.004217748,0.007895973,0.02238273,0.003238541,0.01006253,0.006224268],"category_scores_gemma":[0.04324871,0.0006044224,0.0004981473,0.003972809,0.06486376,0.05416407,0.009199859,0.01187352,0.001079961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007543277,"about_ca_system_score_gemma":0.006562111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004746231,"about_ca_topic_score_gemma":0.003776165,"domain_scores_codex":[0.9634269,0.0267759,0.001213118,0.002635008,0.004839702,0.001109251],"domain_scores_gemma":[0.8538674,0.1301146,0.002112474,0.003624159,0.006839053,0.00344244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004094208,0.0001048251,0.001578085,0.0004086058,0.00001895223,0.0001502274,0.009653233,0.0003361735,0.00008216989,0.9165982,0.008409516,0.06261904],"study_design_scores_gemma":[0.00001947599,0.00007636481,0.001391985,0.0009308286,0.00001101485,0.0003106793,0.02451598,0.001867717,0.0001556694,0.8872358,0.08343685,0.00004774847],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01348954,0.1111312,0.02889769,0.7334706,0.002162406,0.00009949422,0.0000491737,0.00008032828,0.1106195],"genre_scores_gemma":[0.8212858,0.05474963,0.02987833,0.07346671,0.009259435,0.0004780636,0.00005075365,0.0001733731,0.0106579],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03587303,"threshold_uncertainty_score":0.1897169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0631887852266333,"score_gpt":0.3586707018707739,"score_spread":0.2954819166441406,"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."}}