{"id":"W2101997973","doi":"10.1109/icde.2007.369000","title":"On MBR Approximation of Histories for Historical Queries: Expectations and Limitations","year":2007,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Heuristic; Bounding overwatch; Index (typography); Set (abstract data type); Pairwise comparison; Filter (signal processing); Volume (thermodynamics); Search engine indexing; Algorithm; Mathematical optimization; Data mining; Mathematics; Information retrieval; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001566052,0.00004722241,0.00006434674,0.0001129023,0.00008589752,0.00003082553,0.0001412017,0.00001587061,0.000001483459],"category_scores_gemma":[0.0001683285,0.00004320122,0.00001945536,0.0001581232,0.00002174026,0.0004407757,0.00003547489,0.0000181439,0.000001575501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008224077,"about_ca_system_score_gemma":0.00001178919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001057737,"about_ca_topic_score_gemma":0.00003066621,"domain_scores_codex":[0.9995192,0.000005299474,0.0001454874,0.0001352326,0.0001031174,0.00009168535],"domain_scores_gemma":[0.9993568,0.0003386602,0.00005318347,0.0001612641,0.00006095815,0.00002912632],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003585859,0.00003850728,0.00001084859,0.00001064456,0.00000390407,1.671885e-7,0.001435151,0.000001855441,0.00003788035,0.9544073,0.005333177,0.03871701],"study_design_scores_gemma":[0.003089918,0.002258824,0.006066006,0.00005268331,0.00008170884,0.000005069752,0.01006587,0.1456058,0.007350687,0.282063,0.5421402,0.001220178],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006825836,0.00004720573,0.9867088,0.0008158181,0.0002344021,0.0001415899,0.000001851761,0.00005853915,0.01130919],"genre_scores_gemma":[0.3492019,0.000006608103,0.6459396,0.0001153009,0.0000430744,0.00003747286,0.0000222519,0.000005189801,0.004628551],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6723443,"threshold_uncertainty_score":0.1761695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0429416170546344,"score_gpt":0.2591871809807201,"score_spread":0.2162455639260857,"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."}}