{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006386473,0.00008193707,0.0001294464,0.0001376232,0.0000701993,0.00004310823,0.0001997789,0.0000248883,0.000004301361],"category_scores_gemma":[0.00001328206,0.00007855247,0.00000925632,0.0002461822,0.00007889338,0.0002145517,0.00008727625,0.00009125943,8.854861e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001938359,"about_ca_system_score_gemma":0.00004284444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001017727,"about_ca_topic_score_gemma":0.00003577257,"domain_scores_codex":[0.9992531,0.00006959869,0.0002159932,0.0002283518,0.00008666942,0.0001462721],"domain_scores_gemma":[0.999574,0.00009907865,0.00004332767,0.0002214034,0.00002448705,0.00003770354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002409705,0.00007849682,0.002919078,0.00002067259,0.000002947631,0.000001368767,0.002288984,0.0001509152,0.006984067,0.7779801,0.000001672993,0.2095476],"study_design_scores_gemma":[0.001275874,0.000405115,0.03494614,0.00008603881,0.00001803945,0.00005461579,0.003154048,0.03217053,0.01873603,0.9080908,0.0005266498,0.000536177],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.773567,0.0003630597,0.2249878,0.000331175,0.0000115944,0.0002556362,0.000002941154,0.00002423264,0.0004566112],"genre_scores_gemma":[0.9943386,0.00004341934,0.005460233,0.00007653346,0.00002319065,0.00001792349,0.000002746911,0.00000325514,0.00003411986],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2207716,"threshold_uncertainty_score":0.3203277,"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."}}