{"id":"W2182362006","doi":"","title":"Related Entity Finding: University of Waterloo at TREC 2010 Entity Track.","year":2010,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Track (disk drive); Information retrieval; Natural language processing","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":[0.006189238,0.002350236,0.002237169,0.008518437,0.005720084,0.004731575,0.00380979,0.002216997,0.07457094],"category_scores_gemma":[0.0154307,0.001029788,0.0005942168,0.008893444,0.001113046,0.007246139,0.002306726,0.002293748,0.03767782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01106996,"about_ca_system_score_gemma":0.017206,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.6384469,"about_ca_topic_score_gemma":0.7958329,"domain_scores_codex":[0.9955504,0.0007208087,0.0002832847,0.0008779498,0.002116574,0.0004510205],"domain_scores_gemma":[0.9841001,0.00209125,0.0005236336,0.001507608,0.009903226,0.001874175],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004848648,0.00008758451,0.000468487,0.0001564767,0.00001175489,0.00003980691,0.00008916134,0.0001668182,0.0008079002,0.0004701829,0.9820347,0.01561857],"study_design_scores_gemma":[0.0003365595,0.0001056984,0.01171939,0.0001637511,0.00006115976,0.0001135372,0.0005940302,0.006337812,0.0066327,0.002277194,0.9715074,0.000150786],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.01702523,0.006860647,0.01633113,0.01566996,0.002904772,0.003355961,0.7786001,0.02173379,0.1375184],"genre_scores_gemma":[0.02516547,0.001995854,0.02108962,0.001454991,0.0004550975,0.0009353209,0.8422903,0.001571852,0.1050413],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6384469,"threshold_uncertainty_score":0.7273648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0149570952927184,"score_gpt":0.1990470455395193,"score_spread":0.1840899502468009,"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."}}