Earlier quoted context omitted.
I couldn't begin to tell you [1]. It's not a 25% variation, once you've extrapolated from the samples by 10,000x (or whatever). The 25% inter-sample error was on a few replicas of teeny tiny measurements. The post-extrapolation error bars are so wide that they're meaningless. The Smithsonian magazine article is garbage. Ignore it. The paper is saying that they see longitudinal trends in plastic bioaccumulation in var…
I may be misunderstanding you, but it sounds like you're claiming that they had e.g. 10 tiny samples of tissue, that their measurements had an average 25% variation across those 10 samples, and that therefore the whole brain estimate (mass 10,000x that of a single sample) therefore has a much greater uncertainty. But doesn't the standard error of the mean get reduced by the square root of the number of samples? i.e.…
The paper isn't clear what they mean when they said "~25% within-sample coefficient of variation", so I can't directly address what you're asking, but it's tangential to the point I'm making. My naïve interpretation is that they did an ANOVA, and reported the within-group variance, or something similar.
All I'm saying in my footnote is that, whatever the final point estimate, scaling it by a factor of C will affect the variance of the final sample distribution by C^2. So for example, if you have an 8% variance on the measurement at ug/g, and you scale it by 1300 (for 1300g; what the interwebs tells me is the mass of a standard human brain), then you'd expect the variance of the scaled measurement to be 1300^2 * 8%.
That makes a ton of assumptions that probably don't hold in practice -- and I expect the real error to be larger -- but illustrates the point.