{"id":"W2343741375","doi":"","title":"Application of Genre Theory in College-English Reading","year":2016,"lang":"en","type":"article","venue":"Higher education of social science","topic":"Discourse Analysis in Language Studies","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Argumentative; Sentence; Exposition (narrative); Computer science; Linguistics; Syntax; Reading (process); Competence (human resources); Narrative; Reading comprehension; Perspective (graphical); College English; Word order; Psychology; Artificial intelligence; Literature; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004225459,0.00004340534,0.00009954064,0.0001319832,0.0001470182,0.00001248956,0.000185239,0.00001325713,0.0009365266],"category_scores_gemma":[0.00006369722,0.00003067918,0.00002833621,0.0002023572,0.001131822,0.0002503608,0.0000283277,0.00001932817,0.00000645912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006182428,"about_ca_system_score_gemma":0.0001216872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000933827,"about_ca_topic_score_gemma":0.00002328443,"domain_scores_codex":[0.9993883,0.00002575186,0.0001659848,0.0001227129,0.000202091,0.00009513111],"domain_scores_gemma":[0.9993595,0.00005462101,0.0001437241,0.00009573174,0.0003315146,0.00001486161],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.000002554378,0.00006474656,0.001077005,0.000006420994,0.00000433803,1.672763e-8,0.03643111,8.546483e-8,0.001490096,0.9560213,0.0005936038,0.004308742],"study_design_scores_gemma":[0.0005321603,0.00006569949,0.3815136,0.0002128916,0.00009693161,1.472772e-7,0.2591103,0.000003273484,0.006911698,0.1139138,0.2371769,0.0004625542],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1109234,0.0001071447,0.00002860472,0.0003220556,0.0005430394,0.00009994717,0.00001353391,0.00001305823,0.8879492],"genre_scores_gemma":[0.9788348,0.00000494279,0.0000338499,0.00003982231,0.0003487913,0.00002082891,7.50961e-7,0.000002989671,0.02071323],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8679114,"threshold_uncertainty_score":0.9999768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01498260792773982,"score_gpt":0.3003194163101848,"score_spread":0.285336808382445,"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."}}