{"id":"W2397770075","doi":"","title":"Sagan in TAC2009: Using Support Vector Machines in Recognizing Textual Entailment and TE Search Pilot task","year":2009,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Topic Modeling","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Textual entailment; Logical consequence; Computer science; Artificial intelligence; Natural language processing; Pascal (unit); Classifier (UML); Support vector machine; Set (abstract data type); Task (project management); Semantic similarity; Programming language","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":[0.003175744,0.001108175,0.00116717,0.002211347,0.0008411823,0.001480648,0.001797045,0.001938833,0.003846737],"category_scores_gemma":[0.007956866,0.0003301889,0.0006415744,0.001504133,0.0003954272,0.003121728,0.001297216,0.001387343,0.003401903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006736334,"about_ca_system_score_gemma":0.001016631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009291537,"about_ca_topic_score_gemma":0.01257792,"domain_scores_codex":[0.9983568,0.0005814591,0.0001607118,0.0003363447,0.0004395099,0.0001252244],"domain_scores_gemma":[0.997606,0.001033282,0.0001522707,0.0004624811,0.0006133246,0.0001326063],"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":[0.001627639,0.000878558,0.006408363,0.0004603923,0.0002249716,0.0006123292,0.0004016256,0.01394409,0.02583815,0.003382874,0.0939145,0.8523065],"study_design_scores_gemma":[0.0003490005,0.001030581,0.008449531,0.00008116807,0.0002548421,0.001059099,0.0005197697,0.8455348,0.05615018,0.01356906,0.07286111,0.000140998],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3495178,0.003355354,0.4703686,0.001604528,0.0007436649,0.00147984,0.01286802,0.1412102,0.018852],"genre_scores_gemma":[0.4838428,0.0005004282,0.4589411,0.0007525769,0.0001966295,0.0005628204,0.04495207,0.001026089,0.009225467],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009291537,"threshold_uncertainty_score":0.01847488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02585380174903698,"score_gpt":0.2879791380683192,"score_spread":0.2621253363192823,"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."}}