{"id":"W2572785564","doi":"","title":"WaterlooClarke: TREC 2015 Temporal Summarization Track.","year":2015,"lang":"en","type":"article","venue":"Text REtrieval Conference","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Automatic summarization; Computer science; Track (disk drive); Multi-document summarization; Information 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004229312,0.002339002,0.001903537,0.005931581,0.002466084,0.003956087,0.00328958,0.001533863,0.05565622],"category_scores_gemma":[0.008653516,0.0006817976,0.0009297352,0.005481855,0.0008557058,0.005237808,0.001852506,0.002205516,0.03810326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004214403,"about_ca_system_score_gemma":0.01020843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2007812,"about_ca_topic_score_gemma":0.394282,"domain_scores_codex":[0.9978922,0.0005455954,0.0002219027,0.0003373482,0.00079961,0.0002033503],"domain_scores_gemma":[0.9914004,0.001073123,0.0003888904,0.0008902984,0.00565314,0.000594245],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000897623,0.00004223484,0.00009397145,0.0003392551,0.00003131018,0.00002195524,0.00003091272,0.0002553123,0.002122566,0.0004503889,0.9723853,0.02413686],"study_design_scores_gemma":[0.0002836052,0.0001630016,0.0043053,0.0002951408,0.0001946333,0.0001356502,0.0003338899,0.008324075,0.0143377,0.003592112,0.9679184,0.0001164128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.009614935,0.01873,0.06440945,0.01281799,0.007839403,0.002585698,0.7714569,0.04199877,0.07054693],"genre_scores_gemma":[0.009979734,0.003177332,0.05017483,0.001409443,0.0007833684,0.0008625587,0.8469881,0.002317729,0.08430685],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2007812,"threshold_uncertainty_score":0.3992251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04169239849904795,"score_gpt":0.2977835804427729,"score_spread":0.256091181943725,"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."}}