{"id":"W2397624501","doi":"","title":"BUPTTeam Participation at TAC 2011 Recognizing Textual Entailment.","year":2011,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Textual entailment; Logical consequence; Task (project management); Computer science; Natural language processing; Similarity (geometry); Artificial intelligence; Information retrieval; Engineering","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":[0.01064082,0.001792832,0.00190003,0.002338259,0.002557105,0.002975368,0.00374665,0.003579251,0.02226547],"category_scores_gemma":[0.02702108,0.0007279481,0.001025837,0.001024238,0.0008148215,0.004486157,0.004749877,0.003060203,0.01481271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001381358,"about_ca_system_score_gemma":0.002104495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009223177,"about_ca_topic_score_gemma":0.01220456,"domain_scores_codex":[0.9909573,0.00403577,0.000423878,0.001291428,0.002595649,0.0006960086],"domain_scores_gemma":[0.9866049,0.005655847,0.0001906113,0.002437842,0.003702353,0.00140843],"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":[0.003274695,0.003048419,0.004912207,0.001067071,0.0003711507,0.00139998,0.003372371,0.003914197,0.04717899,0.003710405,0.3234378,0.6043127],"study_design_scores_gemma":[0.001647518,0.00500316,0.02690881,0.0002855679,0.0004063471,0.003642594,0.003306619,0.1692501,0.1273294,0.009752731,0.6519225,0.0005445636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3607515,0.004633158,0.3190911,0.01074927,0.00755218,0.007782406,0.04173202,0.07689706,0.1708113],"genre_scores_gemma":[0.4906347,0.000765608,0.2498342,0.002330235,0.001173337,0.004594902,0.1194629,0.004725117,0.1264789],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02226547,"threshold_uncertainty_score":0.07448542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03309136383885607,"score_gpt":0.2625577714443959,"score_spread":0.2294664076055398,"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."}}