{"id":"W1974442733","doi":"10.5539/ijel.v2n3p64","title":"Metadiscoursal Markers in Medical and Literary Texts","year":2012,"lang":"en","type":"article","venue":"International Journal of English Linguistics","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Linguistics; Test (biology); Significant difference; Medical literature; Literature; Psychology; Statistics; Mathematics; Biology; Art; Medicine; Philosophy; Pathology; Botany","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":[0.002727158,0.0002503724,0.0004285938,0.0134928,0.00132378,0.003156732,0.0004195061,0.0004547663,0.002185134],"category_scores_gemma":[0.02598954,0.0002370404,0.0002652859,0.01008028,0.001647113,0.002790744,0.001996733,0.0004652004,0.000299439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001085329,"about_ca_system_score_gemma":0.0007212768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004952874,"about_ca_topic_score_gemma":0.0007387665,"domain_scores_codex":[0.9957092,0.001924029,0.0008490884,0.0004212884,0.0009745334,0.000121906],"domain_scores_gemma":[0.9605202,0.02848453,0.005654277,0.001577426,0.002918459,0.000845121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.002566406,0.0004133811,0.1785242,0.005619303,0.0002353785,0.004376877,0.4020058,0.0006952236,0.04224982,0.04473457,0.002384886,0.3161941],"study_design_scores_gemma":[0.0001096469,0.0008310887,0.5761832,0.002663168,0.0004156014,0.01098424,0.231748,0.004399171,0.02106681,0.01815451,0.1332266,0.0002179298],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9800463,0.004036688,0.005933728,0.0003780593,0.00008063931,0.0001333632,0.0004665338,0.00006316842,0.008861637],"genre_scores_gemma":[0.9913269,0.000887673,0.006278617,0.0000330414,0.00005306448,0.0001018239,0.0003382512,0.00001980967,0.0009608346],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0134928,"threshold_uncertainty_score":0.01442271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0140155748697388,"score_gpt":0.2926779781701875,"score_spread":0.2786624033004487,"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."}}