{"id":"W2122560301","doi":"10.5539/cis.v6n4p125","title":"Abstract Sentence Classification for Scientific Papers Based on Transductive SVM","year":2013,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Ministry of Education, India","keywords":"Computer science; Sentence; Support vector machine; Artificial intelligence; Natural language processing; Machine learning; Pattern recognition (psychology)","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":[0.001927979,0.0007279377,0.001006904,0.00279444,0.0004406233,0.001715002,0.0008094724,0.000759878,0.001825954],"category_scores_gemma":[0.005592247,0.0001798333,0.0008513705,0.001714247,0.0002532182,0.00138179,0.0004984699,0.001008392,0.001191528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005652503,"about_ca_system_score_gemma":0.0005790488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006974942,"about_ca_topic_score_gemma":0.0006499407,"domain_scores_codex":[0.9985155,0.0004788928,0.0002067735,0.0002451394,0.000450638,0.0001029612],"domain_scores_gemma":[0.996467,0.001546326,0.0002522883,0.0002255458,0.001371994,0.0001367652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009147817,0.0007502006,0.008985237,0.0005000067,0.0001847436,0.0002718597,0.0003297317,0.02809625,0.03130165,0.003062327,0.01195737,0.9136458],"study_design_scores_gemma":[0.00002725245,0.0002443391,0.004208049,0.00002677849,0.00006830129,0.0000837359,0.0001409089,0.9801931,0.00956129,0.003150043,0.002271286,0.00002495667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3026145,0.002725981,0.678721,0.001039084,0.0009761174,0.0004528499,0.001463498,0.005559723,0.006447227],"genre_scores_gemma":[0.8059157,0.0005799608,0.1851612,0.000188871,0.0005338316,0.0003492934,0.003876329,0.0001176345,0.003277025],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00279444,"threshold_uncertainty_score":0.01019627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02469599365470214,"score_gpt":0.2510553728594687,"score_spread":0.2263593792047666,"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."}}