{"id":"W2403963403","doi":"","title":"University of Waterloo at the TREC 2013 Temporal Summarization Track.","year":2013,"lang":"en","type":"article","venue":"Text REtrieval Conference","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Automatic summarization; Computer science; Cosine similarity; Ranking (information retrieval); Relevance (law); Information retrieval; Similarity (geometry); Metric (unit); Latency (audio); Set (abstract data type); Task (project management); Relevance feedback; Artificial intelligence; Natural language processing; Data mining; Pattern recognition (psychology); Image retrieval","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002058725,0.00009778421,0.0001304006,0.00003878466,0.0001343774,0.0000754197,0.0008698694,0.00006653543,0.0007182094],"category_scores_gemma":[0.00002800289,0.00007359836,0.00004609625,0.0002200113,0.0001063779,0.0004529409,0.0003094277,0.0001102711,0.0002962813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004518118,"about_ca_system_score_gemma":0.00009218318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002671066,"about_ca_topic_score_gemma":0.0003188055,"domain_scores_codex":[0.9990389,0.00008619428,0.0001627098,0.0002744761,0.0002516594,0.0001860362],"domain_scores_gemma":[0.9989803,0.00007018517,0.0001141454,0.0005536809,0.0002192514,0.0000624352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002680703,0.0004125917,0.07044587,0.0002450254,0.0001857774,0.00004590785,0.04280965,0.001784343,0.1003878,0.209527,0.02848265,0.5454054],"study_design_scores_gemma":[0.001482248,0.0003074159,0.05541895,0.00009984506,0.00003915047,0.00002156853,0.0009818118,0.8801074,0.03026914,0.01339291,0.0170923,0.0007872606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7155336,0.00006126202,0.2786462,0.002925819,0.0001691955,0.0002719831,0.000003303978,0.00009029989,0.002298359],"genre_scores_gemma":[0.9856659,0.0000294658,0.002889748,0.00005065333,0.00001727424,3.840061e-7,0.000005133591,0.000003793451,0.01133769],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8783231,"threshold_uncertainty_score":0.786389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02757373134767387,"score_gpt":0.2131915196124785,"score_spread":0.1856177882648046,"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."}}