> Despite my great fondness for Mr. Fischer,
> his reliance on the bee book flat baffles me.
It shouldn't - standards matter, moreso in science when one wants to compare
one set of measurements with another, and ever moreso with things like the
specific techniques used to measure pathogen/pest levels.
And if new evidence prompts adjustments of the standards, that's a good
thing. Its progress.
But the inherent inaccuracies in each method (and none are perfect) are
irrelevant to everyone except those who still desperately cling to the
fantasy of a "threshold" ("seasonally-adjusted" or not). Those of us who
make treatment decisions based upon more than one isolated data point don't
need to worry about the well-known less-than complete varroa fall from the
sample.
As long as one uses a consistent method, the counts that result will be
consistent, (some might say consistently low by x %) and we will still be
able to plot the slope of the curve, and it is the slope of the curve that
matters, not the values of the points on the curve.
But beekeepers will continue to ignore such basic things as the difference
between a scalar and a vector, as they insist that beekeepers alone can
somehow have a single-measurement "threshold" that is NOT akin to pests per
acre, or pests per plant, but is instead "pests per hive", about as useful
as "pests per field". ("How big a field?" You ask. That is the correct
question, the one beekeepers keep ducking and tapdancing around.)
As far as the paper itself, it is an outlier, and I tend to bet against
outlier results, unless a clear and compelling narrative is offered to
explain the error made by everyone who came before. Lacking such a
narrative, I am a strong believer in the "normal distribution" (the
bell-shaped curve) as there has yet to be a natural process than did not
produce data in strict adherence to the "normal distribution".
Scalars, vectors, bell curves... beekeepers as a whole and "bee science" as
a whole tends to be weak in math, even actively dislike it. But it still
works for you, even if you don't like it.
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