{"id":"W2290614696","doi":"","title":"DETECTING TEXTUAL ENTAILMENT WITH CONDITIONS ON DIRECTIONAL TEXT RELATEDNESS SCORES","year":2009,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Logical consequence; Textual entailment; Heuristics; Natural language processing; Computer science; Word (group theory); Artificial intelligence; Exploit; Relation (database); Point (geometry); Identity (music); Mathematics; Data mining","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":[0.0002225887,0.0001177096,0.0001164764,0.000101272,0.0005236659,0.00004650091,0.0002667055,0.00004004694,0.00001670851],"category_scores_gemma":[0.00001176244,0.00009752721,0.00002787725,0.0004355432,0.0002328144,0.0002438029,0.00003730222,0.0001108411,0.00001290546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001967231,"about_ca_system_score_gemma":0.0000476874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005962248,"about_ca_topic_score_gemma":0.000001610897,"domain_scores_codex":[0.9992026,0.00004649745,0.0001880675,0.0002738914,0.0001541546,0.0001348048],"domain_scores_gemma":[0.9991012,0.000271327,0.0001092591,0.0003529807,0.0001031165,0.00006208928],"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":[0.00002006411,0.0001478886,0.00006751648,0.000004050728,0.00001361269,2.8165e-7,0.0001921781,0.0002834017,0.0007904909,0.9753682,0.00005096609,0.02306132],"study_design_scores_gemma":[0.0004256793,0.0004065721,0.01665147,0.00003488607,0.00003137709,0.00006190877,0.0008561588,0.0006058697,0.01386909,0.9626842,0.00408365,0.0002891247],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05847157,0.0003261916,0.9282523,0.00135494,0.00002707214,0.0005467434,0.00002960162,0.0002373521,0.01075418],"genre_scores_gemma":[0.9940521,0.00003331356,0.005138393,0.0001173033,0.00005230182,0.0002321026,0.00002047859,0.000004843075,0.000349201],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9355805,"threshold_uncertainty_score":0.4027667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006591953534917815,"score_gpt":0.2391887279973494,"score_spread":0.2325967744624315,"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."}}