{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000427929,0.00007648711,0.00009949059,0.00004758253,0.0001743581,0.0000362793,0.0002925801,0.00004495964,0.00002081371],"category_scores_gemma":[0.00001119696,0.00006879092,0.00002278382,0.0001088697,0.0001134858,0.0001681117,0.0001494868,0.000079655,0.00003919435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001361747,"about_ca_system_score_gemma":0.00002047942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001010186,"about_ca_topic_score_gemma":0.000006927443,"domain_scores_codex":[0.9993713,0.00004157168,0.0001794985,0.0002058515,0.00009938495,0.0001023588],"domain_scores_gemma":[0.9992432,0.000123767,0.0000885975,0.0004298642,0.00006689277,0.00004766342],"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":[0.000006337485,0.00001682848,0.00004537784,0.00003011241,0.000005488923,1.552122e-7,0.000341879,0.00000318764,0.005421625,0.9227982,0.00001799517,0.07131286],"study_design_scores_gemma":[0.0005024467,0.00006926133,0.0004751747,0.00003007813,0.00004747468,0.00008240376,0.002397303,0.002598053,0.1763249,0.799185,0.01788227,0.0004057035],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2738926,0.0001054757,0.717361,0.0001613876,0.00009294687,0.0002825731,0.000009846387,0.0001341459,0.007959954],"genre_scores_gemma":[0.9936613,0.000009742579,0.005714847,0.00002883397,0.0000777302,0.0001534326,0.00001019973,0.00000417101,0.0003397418],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7197687,"threshold_uncertainty_score":0.2805212,"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."}}