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 "title": "Baseline Capture Architecture for Learned Scientific Triggers: A Control Plane for Measuring Selection Before Irreversible Data Loss (EA-SEI-BCA-01 v1.0)",
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 "description": "Proposes the Baseline Capture Architecture (BCA): a statistically interpretable control plane for scientific instruments that perform irreversible learned selection before durable storage. BCA separates four functions usually conflated — a content-independent probability sample taken before the audited selection; a full-rate tap of the exact representation presented to the selector; probability-weighted enrichment channels that increase coverage of rare regions without sacrificing inferential validity; and shadow selectors whose decisions are recorded but do not control acquisition. The resulting replay bank permits retrospective estimation of retention surfaces, directional assimilation, model-to-model miss correlation (the RII family), and selection drift across successive classifier generations.\n\nThe central principle is deliberately minimal: under a finite storage budget, no content-sensitive algorithm can constitute a less assumption-laden baseline than a probability sample whose inclusion mechanism is independent of event content and whose inclusion probability is known. More elaborate models may improve discovery efficiency, but they belong to the experimental arm, not the control arm. BCA does not propose an alternative trigger; it proposes the missing control group for a trigger. Existing CMS infrastructure — Zero Bias data used to train AXOL1TL, Level-1 Data Scouting capturing trigger information at the full 40 MHz while bypassing ordinary Level-1 selection, and the Global Trigger test crate receiving live inputs without determining readout — demonstrates that each component is individually feasible; the contribution is to bind them into a common metrological architecture whose purpose is measuring what irreversible learned selection fails to retain.",
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