{"id":"W2170896764","doi":"10.1109/icde.2009.102","title":"Ranking with Uncertain Scores","year":2009,"lang":"en","type":"article","venue":"Proceedings - International Conference on Data Engineering","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":83,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Ranking (information retrieval); Computer science; Rank (graph theory); Data mining; Set (abstract data type); Uncertain data; Probabilistic logic; Learning to rank; Semantics (computer science); Information retrieval; Ranking SVM; Sampling (signal processing); Machine learning; Artificial intelligence; Mathematics","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":[0.008682693,0.001152383,0.002684501,0.00303083,0.001158447,0.004877107,0.002313565,0.001473832,0.00263945],"category_scores_gemma":[0.03693526,0.0007905192,0.001325283,0.005722911,0.001358664,0.007116942,0.002294905,0.001659567,0.0009380834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001902116,"about_ca_system_score_gemma":0.001667536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002582267,"about_ca_topic_score_gemma":0.003629468,"domain_scores_codex":[0.9860587,0.005429043,0.001033331,0.00174156,0.005046347,0.0006911205],"domain_scores_gemma":[0.9735906,0.01628808,0.001940479,0.004793686,0.002811766,0.000575484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009446207,0.0002990011,0.009777337,0.0005165802,0.000298607,0.0004200246,0.0005793027,0.4870932,0.006014702,0.158277,0.00695454,0.3288251],"study_design_scores_gemma":[0.00005200738,0.0002448764,0.001314405,0.00003191925,0.00006040607,0.0003156856,0.0001839275,0.8849777,0.003198925,0.1057994,0.003756967,0.00006383233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05265028,0.0005954988,0.9432284,0.000473721,0.00005152548,0.0001593418,0.0004713571,0.0006083435,0.001761488],"genre_scores_gemma":[0.5306363,0.0006166513,0.4633922,0.0001860825,0.0002193887,0.0002596477,0.001558181,0.0001770512,0.002954453],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008682693,"threshold_uncertainty_score":0.04591906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05920357991017572,"score_gpt":0.2793436375045019,"score_spread":0.2201400575943261,"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."}}