{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001023191,0.00007560731,0.0001316209,0.0002131239,0.00001304534,0.00009660995,0.0007106799,0.00005831207,0.00002647035],"category_scores_gemma":[0.03242885,0.00006698929,0.00004333572,0.00007000319,0.00003085515,0.0002785225,0.0001900663,0.0002946691,0.000001244518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005141821,"about_ca_system_score_gemma":0.0001077749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004017825,"about_ca_topic_score_gemma":0.000001956085,"domain_scores_codex":[0.9985799,0.00005171551,0.0004222198,0.0000891202,0.0006970552,0.0001600535],"domain_scores_gemma":[0.9967114,0.0002452611,0.0001699729,0.000103742,0.002579838,0.0001897784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001375133,0.0007697645,0.1817876,0.00004317474,0.0004630551,0.001529744,0.01326712,0.0004964043,0.00001300348,0.5904469,0.007346003,0.2036997],"study_design_scores_gemma":[0.005019401,0.0002475495,0.06008057,0.0009210479,0.00006760865,0.00043199,0.0003954341,0.09922775,0.0003030837,0.05276196,0.7796897,0.0008539351],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.299946,0.01371243,0.416998,0.001745501,0.1709905,0.0002135786,0.00001510579,0.0001148244,0.09626405],"genre_scores_gemma":[0.9598457,0.00008384497,0.03048855,0.0003176149,0.009234577,4.005548e-7,6.199464e-7,0.000005223543,0.00002344038],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7723436,"threshold_uncertainty_score":0.9757214,"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."}}