{"id":"W2951602292","doi":"10.5539/ijel.v9n4p51","title":"A Corpus-Based Comparative Study of Chinese EFL Learners’ Use of Temporal Metaphor in English","year":2019,"lang":"en","type":"article","venue":"International Journal of English Linguistics","topic":"Language, Metaphor, and Cognition","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Metaphor; Linguistics; Conceptual metaphor; Psychology; Computer science; Natural language processing; 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":[0.001554602,0.0003238228,0.0004830283,0.002902712,0.002507956,0.001063235,0.0004022687,0.0003875895,0.002319054],"category_scores_gemma":[0.003198397,0.0001733143,0.0001768114,0.002598652,0.001417578,0.001312508,0.001468887,0.000460132,0.0001885871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001261438,"about_ca_system_score_gemma":0.001154065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01777375,"about_ca_topic_score_gemma":0.03277699,"domain_scores_codex":[0.9995659,0.0001426489,0.00005405546,0.0000934196,0.00007917054,0.0000647907],"domain_scores_gemma":[0.9980983,0.00113042,0.0002169109,0.0001393089,0.0002825962,0.0001324656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0001732861,0.0005455312,0.1264701,0.0004869002,0.00003834817,0.002320296,0.8208039,0.0000956316,0.01555766,0.001689152,0.0006325711,0.03118662],"study_design_scores_gemma":[0.00002603369,0.0003744079,0.5641417,0.0001413786,0.00006144607,0.002204416,0.416657,0.0004973736,0.004270331,0.0003849873,0.01116895,0.00007195305],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988738,0.0000863872,0.00008049171,0.00001618519,0.000001855099,0.00001895481,0.00005295479,9.886977e-7,0.0008683133],"genre_scores_gemma":[0.9984718,0.0002145224,0.0003788855,0.00002526873,0.000003487285,0.00006230942,0.0001761131,0.000004682722,0.0006629616],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01777375,"threshold_uncertainty_score":0.03534061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03970417483166177,"score_gpt":0.3471644219609707,"score_spread":0.3074602471293089,"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."}}