{"id":"W2158685682","doi":"10.5539/elt.v4n4p245","title":"Study on Lexical Cohesion in English and Persian Research Articles (A Comparative Study)","year":2011,"lang":"en","type":"article","venue":"English Language Teaching","topic":"Lexicography and Language Studies","field":"Arts and Humanities","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Islamic Azad University","keywords":"Cohesion (chemistry); Persian; Linguistics; Lexical density; Collocation (remote sensing); Natural language processing; Computer science; Foreign language; Psychology; Artificial intelligence; Lexical item","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00194518,0.0002098589,0.0003054155,0.0004282767,0.0007454188,0.0002030624,0.0002038371,0.00003941874,0.0003351448],"category_scores_gemma":[0.0005160073,0.000164638,0.00005269885,0.0001119115,0.0002949056,0.0003050605,0.0001635313,0.001029543,0.00001184005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003695429,"about_ca_system_score_gemma":0.000009732242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002709215,"about_ca_topic_score_gemma":0.01023191,"domain_scores_codex":[0.9974189,0.0010658,0.0002659004,0.0004308753,0.0004039068,0.000414585],"domain_scores_gemma":[0.9991319,0.0003604201,0.00004634856,0.00027939,0.00009519855,0.00008678502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001021303,0.002024195,0.03117157,0.000008402683,0.00006578108,0.0002213405,0.9527785,3.083227e-7,0.000008993215,0.01194708,0.0001579579,0.001513732],"study_design_scores_gemma":[0.001056238,0.001393614,0.02274111,0.00005279732,0.00002033962,4.108103e-7,0.9737906,0.0000042711,0.00005821757,0.00009762163,0.0005859335,0.0001988422],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8272455,0.0002959823,4.487663e-7,0.00000925484,0.0002156394,0.0006037445,0.000007165087,0.0001533819,0.1714689],"genre_scores_gemma":[0.9987574,0.000001907352,0.00002848201,0.00004024175,0.0007047955,0.00006072861,0.00000229771,0.00001978245,0.0003844162],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1715118,"threshold_uncertainty_score":0.6713742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1782260787640585,"score_gpt":0.3574114319799999,"score_spread":0.1791853532159414,"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."}}