{"id":"W2296709160","doi":"","title":"GUCAS at TREC 2011 Microblog Track.","year":2011,"lang":"en","type":"article","venue":"Text REtrieval Conference","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Microblogging; Social media; Context (archaeology); Relevance (law); Track (disk drive); Probabilistic logic; Information retrieval; Baseline (sea); Natural language processing; Query expansion; Artificial intelligence; Language model; World Wide Web","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":[0.007395474,0.003923351,0.002864947,0.006008905,0.00394399,0.003822803,0.002702709,0.002966729,0.03237714],"category_scores_gemma":[0.0148717,0.0007151858,0.00110931,0.00353243,0.000658425,0.006441104,0.002290321,0.003052769,0.01968764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006484662,"about_ca_system_score_gemma":0.00554504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1770106,"about_ca_topic_score_gemma":0.247325,"domain_scores_codex":[0.9952023,0.00160817,0.0002430152,0.0006672394,0.001795791,0.0004835608],"domain_scores_gemma":[0.9892335,0.002483814,0.0003684731,0.001511402,0.004745489,0.001657298],"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":[0.0003460523,0.0006948509,0.0006904686,0.0005582514,0.00008969654,0.00008174958,0.00006531863,0.001113289,0.002131268,0.0005342386,0.9588061,0.0348888],"study_design_scores_gemma":[0.001928244,0.001531166,0.02168546,0.0003047897,0.000410389,0.0003830646,0.001043748,0.09617876,0.03209002,0.003328566,0.840717,0.0003989507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1056494,0.01416838,0.02120934,0.014621,0.01703408,0.006981912,0.665282,0.07146175,0.08359205],"genre_scores_gemma":[0.08192725,0.002690453,0.04146848,0.002149773,0.00200006,0.003373956,0.7520476,0.002332142,0.1120104],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1770106,"threshold_uncertainty_score":0.3519605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09059576231893875,"score_gpt":0.2552359157374093,"score_spread":0.1646401534184706,"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."}}