{"id":"W2401761585","doi":"","title":"TAC 2010 Summarization Track - Update Summarization with Interview Algorithm.","year":2010,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Automatic summarization; Computer science; Track (disk drive); Algorithm; Data mining; Information retrieval; Artificial intelligence; Operating system","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":[0.002536437,0.001295886,0.001228572,0.004998607,0.001388201,0.00245513,0.002234467,0.00134937,0.01615932],"category_scores_gemma":[0.01241948,0.0004502598,0.0007996686,0.004431469,0.0003773511,0.00302331,0.001833983,0.001399898,0.01390171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008350629,"about_ca_system_score_gemma":0.002180699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006201318,"about_ca_topic_score_gemma":0.009780661,"domain_scores_codex":[0.9977411,0.0007058689,0.0002470459,0.0004574647,0.000652568,0.0001959215],"domain_scores_gemma":[0.9949701,0.0009510494,0.0002073583,0.001106251,0.002618252,0.0001469783],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006181653,0.0001415736,0.0007612415,0.0003998436,0.0001214143,0.00008447002,0.0002620042,0.005044159,0.009961334,0.005905197,0.2211131,0.7555875],"study_design_scores_gemma":[0.0004293391,0.0008115742,0.004134316,0.000178602,0.0003815973,0.0005614501,0.0009347346,0.4007646,0.06963687,0.04001461,0.4819389,0.0002133529],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.01145194,0.001289765,0.9109491,0.0007830469,0.001053378,0.00118534,0.01861499,0.04217003,0.01250251],"genre_scores_gemma":[0.07261875,0.0005146678,0.8081295,0.0003674987,0.0003983205,0.001362293,0.08428209,0.002022452,0.03030432],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.01615932,"threshold_uncertainty_score":0.05405831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00681810827578963,"score_gpt":0.2289243610219437,"score_spread":0.2221062527461541,"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."}}