{"id":"W7125607357","doi":"10.1109/cascon66301.2025.00113","title":"Resilient LLM-DBMS Pipelines via Event-Driven Fallback Orchestration","year":2025,"lang":"","type":"article","venue":"","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cache; Database transaction; Pipeline transport; Latency (audio); Guard (computer science); Service provider","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003020279,0.0008199599,0.0004414162,0.0006963704,0.0007148246,0.001712108,0.002448397,0.0006190074,0.003136343],"category_scores_gemma":[0.006430488,0.0006248811,0.0003317129,0.0004529873,0.0009643605,0.002720921,0.003662744,0.001563512,0.000895622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001137628,"about_ca_system_score_gemma":0.00157244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003076455,"about_ca_topic_score_gemma":0.003311518,"domain_scores_codex":[0.9978639,0.0003690422,0.0001551991,0.0004920063,0.0006976015,0.0004222544],"domain_scores_gemma":[0.9961573,0.000646017,0.0003400584,0.001904043,0.000500054,0.0004524887],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004008852,0.001693902,0.04635954,0.0005520647,0.0002506087,0.0008195891,0.001858623,0.1862209,0.2123489,0.03247782,0.02623564,0.4871736],"study_design_scores_gemma":[0.0001774073,0.0006611591,0.004343507,0.00004517035,0.00006357101,0.0002859052,0.0003593006,0.8903334,0.06133409,0.02076061,0.02155862,0.00007735924],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2836875,0.0006481679,0.645722,0.001214306,0.0001808814,0.0007604996,0.0006692451,0.05950983,0.007607561],"genre_scores_gemma":[0.8860757,0.000107219,0.109931,0.0003025493,0.00003281579,0.0001629369,0.0005108097,0.0005314547,0.002345515],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003136343,"threshold_uncertainty_score":0.01597291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07227232351950855,"score_gpt":0.3992964455790771,"score_spread":0.3270241220595685,"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."}}