{"id":"W2916496076","doi":"","title":"AUEB at TAC 2009","year":2009,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"WordNet; Computer science; Natural language processing; Parsing; Artificial intelligence; Dependency grammar; Textual entailment; Classifier (UML); Dependency (UML); Similarity measure; Logical consequence","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":[0.006970697,0.0007953971,0.000936397,0.001149239,0.002929005,0.004616274,0.001837652,0.001467267,0.09122313],"category_scores_gemma":[0.006826315,0.0003278592,0.0004326773,0.0007521384,0.0005556658,0.002785494,0.002966319,0.002404458,0.0515588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002619806,"about_ca_system_score_gemma":0.00306419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01659592,"about_ca_topic_score_gemma":0.02411203,"domain_scores_codex":[0.996789,0.0008063105,0.00006104042,0.0005992628,0.0009823018,0.0007621053],"domain_scores_gemma":[0.9954072,0.0003602687,0.00007992754,0.000655383,0.001759241,0.001737844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001078085,0.0009366364,0.002317765,0.0001012544,0.00003708146,0.0006450746,0.0007005041,0.001428402,0.006681979,0.01104218,0.8551148,0.1199163],"study_design_scores_gemma":[0.000265675,0.0002900524,0.003853576,0.00006425903,0.00002066564,0.0002527962,0.0005754704,0.01164005,0.004476268,0.008199653,0.9703086,0.00005304018],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.134005,0.004053939,0.1113125,0.04117867,0.02033982,0.002476161,0.05439827,0.03967096,0.5925645],"genre_scores_gemma":[0.3023616,0.0006742337,0.0951065,0.004763138,0.002141465,0.001022525,0.08039892,0.006419324,0.5071123],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.09122313,"threshold_uncertainty_score":0.3051717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005567143108380655,"score_gpt":0.2555055507580412,"score_spread":0.2499384076496605,"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."}}