{"id":"W2942259532","doi":"10.5539/elt.v12n5p194","title":"Analyzing Problem-Causing Factors for Pakistani EFL Learners in Translating Present Indefinite and Past Indefinite Tenses From Urdu Into English","year":2019,"lang":"en","type":"article","venue":"English Language Teaching","topic":"Translation Studies and Practices","field":"Arts and Humanities","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Urdu; Meaning (existential); Linguistics; Psychology; Verb; Test (biology); Mathematics education; Remedial education; Class (philosophy); Set (abstract data type); Semitic languages; Plural; Computer science; Artificial intelligence; Arabic","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":[0.001482615,0.0004758044,0.0004239498,0.001015827,0.00176897,0.002472591,0.00055073,0.000857989,0.00222476],"category_scores_gemma":[0.008532959,0.000321707,0.0003868385,0.0009207245,0.001018986,0.001431514,0.0009481402,0.001057982,0.0005210598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006608022,"about_ca_system_score_gemma":0.001781838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006213624,"about_ca_topic_score_gemma":0.008910946,"domain_scores_codex":[0.9982331,0.0003929239,0.0002330303,0.0001725154,0.0006501342,0.0003183163],"domain_scores_gemma":[0.9924785,0.002289557,0.002573428,0.0001907852,0.001773452,0.0006942539],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001482556,0.0008894681,0.8692778,0.0002075203,0.00002810931,0.004513208,0.08291599,0.0001478746,0.003219085,0.0003633502,0.0009961039,0.03729328],"study_design_scores_gemma":[0.00001861727,0.0006905789,0.6111668,0.0001663059,0.0000864102,0.003733318,0.3735923,0.0008999892,0.003883106,0.0005887884,0.005101057,0.0000725975],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998704,0.0001088239,0.00008718819,0.0001820042,0.000005121344,0.00001482953,0.00001374429,0.00000342805,0.0008809202],"genre_scores_gemma":[0.9982653,0.0002923116,0.0002811959,0.00006322465,0.000005246822,0.0000137863,0.0000336314,0.000003110617,0.001042129],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006213624,"threshold_uncertainty_score":0.01235491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02614563712330185,"score_gpt":0.2724846911549724,"score_spread":0.2463390540316705,"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."}}