{"id":"W4393144966","doi":"10.1109/tkde.2024.3381192","title":"DIBA: A Re-Configurable Stream Processor","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Alexander von Humboldt-Stiftung","keywords":"Computer science; Stream processing; Computer architecture; Parallel computing","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":[0.0008902764,0.0007819143,0.0005597291,0.0009654312,0.0004027575,0.002023028,0.002604327,0.0006537015,0.004569407],"category_scores_gemma":[0.002269172,0.0004733967,0.0004247305,0.000823638,0.0006282332,0.00237011,0.001668355,0.001952532,0.00215132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009378372,"about_ca_system_score_gemma":0.001296072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001798612,"about_ca_topic_score_gemma":0.001255594,"domain_scores_codex":[0.9990028,0.0001114,0.00009632451,0.0002508703,0.0003788629,0.0001598412],"domain_scores_gemma":[0.9988264,0.0001956704,0.00008007094,0.0003664438,0.0003720814,0.0001593416],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004118581,0.001018559,0.01125085,0.0009179252,0.0002475814,0.000914394,0.0008514746,0.03938494,0.3175218,0.03289125,0.08441967,0.5064631],"study_design_scores_gemma":[0.0007825111,0.001060673,0.003296756,0.00008171822,0.0001863365,0.001240344,0.0002234228,0.5659038,0.2142739,0.01122443,0.2015112,0.0002149661],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0546198,0.0007812241,0.8946455,0.0005940089,0.000500386,0.0006729995,0.0009150777,0.0383758,0.008895188],"genre_scores_gemma":[0.5303336,0.0008430482,0.4438977,0.001547068,0.0002776998,0.0008073162,0.003828695,0.00256521,0.01589965],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004569407,"threshold_uncertainty_score":0.01528621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02496765267250139,"score_gpt":0.2756281570028197,"score_spread":0.2506605043303183,"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."}}