Nguyen & Eger's measured arXiv subfields exclude Linnainmaa's own (numerical analysis / automatic differentiation), so their paper supplies no baseline growth figure to run his citation trajectory against
Nguyen & Eger's dataset spans "the science subfields of computer science, physics, mathematics, economics, electrical engineering, quantitative finance, quantitative biology, and statistics," aggregated into three classes — "(i) AI related papers as a subset of computer science (CS), (2) non-AI CS papers and (3) non-CS papers" — with the headline "citation amnesia" result specific to cs.AI, cs.CL, cs.CV and cs.LG. Automatic differentiation / numerical analysis — Linnainmaa's own field, per claim-linnainmaa-field-numerical-analysis — is not among the underlying arXiv categories aggregated into those classes, and is not one of the four AI subfields the amnesia result names.
This bounds what "confirming the field-growth mechanism" can mean for Linnainmaa. Nguyen & Eger's paper carries no bibliography-age or submission-growth number for the field his citers actually come from (mostly applied AD and differentiable programming, a cross-cutting practice — claim-linnainmaa-post-2010-citers-mostly-generic-ad-not-priority-historiography). Their mechanism — fast-growing fields produce citations that skew recent — may apply by analogy, but there is no Nguyen & Eger figure to run Linnainmaa's trajectory against in the way question-linnainmaa-citation-growth-vs-nguyen-eger-joint-test frames it. The published metric measures mean bibliography age across ~300k papers 2013–2022, not the citation-age of inbound citations to one paper; a genuine joint test would require reclassifying his citers into Nguyen & Eger's own categories (most would fall outside their eight fields) or computing an analogous statistic for a field they never studied. This is the methodological limit the existing bridge note flags as a cross-dataset extrapolation, stated here as its own claim with the primary's methodology text.
Source
“We aggregate our different subfields into three classes: (i) AI related papers as a subset of computer science (CS), (2) non-AI CS papers and (3) non-CS papers.”
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