{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003346246,0.0002217256,0.000244887,0.00009796029,0.000140679,0.00009687075,0.001509127,0.0001392478,0.001882605],"category_scores_gemma":[0.00005857723,0.0002062222,0.00009028442,0.0002073922,0.0000959749,0.0004182789,0.0005139979,0.0002358242,0.002017718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008500463,"about_ca_system_score_gemma":0.0001563489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001542434,"about_ca_topic_score_gemma":0.00004495036,"domain_scores_codex":[0.9981552,0.00007620593,0.000340454,0.0006622784,0.0002991899,0.000466708],"domain_scores_gemma":[0.9984109,0.00006467562,0.0001279593,0.001045462,0.0001650455,0.0001859598],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003823305,0.0005556839,0.01237687,0.0001335147,0.0001357136,0.0003945332,0.02647697,0.00001474256,0.09554705,0.587179,0.01034182,0.2664618],"study_design_scores_gemma":[0.004793286,0.001603173,0.08554494,0.0003143582,0.0001348468,0.0008875057,0.0003232624,0.1469687,0.5788514,0.1055851,0.07027108,0.004722336],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3655605,0.0004442319,0.5264538,0.0006315414,0.001027064,0.0003351162,0.000007163046,0.0006072242,0.1049334],"genre_scores_gemma":[0.9614867,0.00003326388,0.02923464,0.0002735789,0.00005920544,0.000003465331,0.000001997017,0.00001280057,0.008894358],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5959262,"threshold_uncertainty_score":0.9990298,"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."}}