{"id":"W2296086669","doi":"","title":"JU_CSE_TAC: Textual Entailment Recognition System at TAC RTE-6","year":2010,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Topic Modeling","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Textual entailment; Computer science; Natural language processing; Task (project management); Sentence; Novelty; Artificial intelligence; Logical consequence; Set (abstract data type); Similarity (geometry); 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":[0.002610408,0.00142835,0.001294456,0.001595083,0.0007641226,0.001881973,0.002513034,0.001935953,0.02538732],"category_scores_gemma":[0.005286777,0.0006546442,0.001214992,0.0009501564,0.0003774801,0.003090281,0.001185534,0.001483606,0.02075721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008696549,"about_ca_system_score_gemma":0.001246384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005018859,"about_ca_topic_score_gemma":0.004717202,"domain_scores_codex":[0.9983079,0.0003456918,0.0001672859,0.0005543989,0.0004612594,0.0001633867],"domain_scores_gemma":[0.9973591,0.0006696819,0.0001315432,0.0005555262,0.001068469,0.0002156896],"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":[0.003956104,0.00133411,0.003154346,0.001222417,0.0002776149,0.001031936,0.0006092626,0.007312842,0.132016,0.003300585,0.3310204,0.5147642],"study_design_scores_gemma":[0.001480849,0.003305182,0.0216444,0.0001415904,0.0004206031,0.003177905,0.0007110099,0.4510662,0.2952169,0.007427775,0.2149385,0.0004691938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"software","genre_gemma":"methods","genre_scores_codex":[0.1947618,0.002173033,0.2961279,0.001306302,0.000893991,0.002116766,0.03615301,0.4161275,0.05033971],"genre_scores_gemma":[0.4409822,0.0004124331,0.3689108,0.0008106796,0.0003930703,0.00132456,0.1348176,0.006481109,0.0458676],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02538732,"threshold_uncertainty_score":0.08492905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01088035976951758,"score_gpt":0.2322026495736445,"score_spread":0.2213222898041269,"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."}}