{"id":"W54772941","doi":"10.1007/978-3-642-38457-8_6","title":"Identifying Explicit Discourse Connectives in Text","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Treebank; Computer science; Parsing; Natural language processing; Artificial intelligence; Syntax; Coherence (philosophical gambling strategy); Head (geology); Linguistics; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00307681,0.001267641,0.0006691253,0.006182094,0.001822802,0.004425102,0.001232228,0.001314547,0.01138755],"category_scores_gemma":[0.01984594,0.0007437596,0.0006213801,0.004101546,0.001577568,0.01141386,0.004552286,0.001843749,0.003605429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001079198,"about_ca_system_score_gemma":0.00145248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001135883,"about_ca_topic_score_gemma":0.001469476,"domain_scores_codex":[0.9958076,0.001786319,0.0004686678,0.0009055074,0.0008322405,0.0001995639],"domain_scores_gemma":[0.9740968,0.02177658,0.001024909,0.000641735,0.002155026,0.0003050487],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001308113,0.0003325246,0.01376529,0.004380372,0.0001917483,0.004143744,0.03174236,0.004179406,0.06228257,0.2534602,0.03472668,0.5894871],"study_design_scores_gemma":[0.0002140526,0.0003013276,0.009087578,0.003034364,0.000750115,0.003059556,0.02905789,0.1669025,0.1023956,0.439109,0.2458596,0.0002285031],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2745983,0.005198806,0.6462583,0.005611491,0.001089877,0.0007063613,0.01181717,0.00533897,0.04938071],"genre_scores_gemma":[0.6840438,0.00194264,0.2862128,0.0003870614,0.0005400263,0.0004471168,0.01456146,0.0007762213,0.01108886],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01138755,"threshold_uncertainty_score":0.03809518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02397065548677053,"score_gpt":0.2931898755067489,"score_spread":0.2692192200199784,"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."}}