{"id":"W2405247712","doi":"","title":"TJU GSummary at TAC2011: Category oriented extractive content selection for guided summarization.","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":false,"ca_institutions":"","funders":"","keywords":"Automatic summarization; Selection (genetic algorithm); Computer science; Content (measure theory); Mathematics; Natural language processing; Artificial intelligence","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":[0.004357969,0.001775387,0.001729445,0.004128126,0.001498497,0.003441746,0.001733389,0.002701843,0.021749],"category_scores_gemma":[0.01561353,0.0006387871,0.001125736,0.003691529,0.0006586554,0.004336901,0.002543581,0.002330113,0.01944815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007646114,"about_ca_system_score_gemma":0.001594596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004314553,"about_ca_topic_score_gemma":0.006926368,"domain_scores_codex":[0.9967065,0.001392323,0.0001798553,0.0005805393,0.0009194422,0.0002213729],"domain_scores_gemma":[0.9915386,0.003431985,0.0001851708,0.001166134,0.003085021,0.0005930667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001036359,0.0003004208,0.0006940273,0.0007638404,0.0001664044,0.0004613331,0.0009482669,0.003451778,0.04706451,0.007443259,0.4766313,0.4610386],"study_design_scores_gemma":[0.0006072099,0.0007222265,0.004411425,0.0002900823,0.0003870743,0.0007441887,0.001287187,0.2396556,0.1367907,0.04705137,0.5676669,0.0003860443],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01666352,0.003154527,0.8855971,0.005926796,0.003560385,0.000908539,0.01585345,0.05419322,0.01414258],"genre_scores_gemma":[0.09748995,0.001508741,0.793397,0.001360578,0.001459028,0.001140198,0.04343674,0.011014,0.04919374],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.021749,"threshold_uncertainty_score":0.0727576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04326973547035373,"score_gpt":0.2589672449241649,"score_spread":0.2156975094538112,"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."}}