Post by Teresita Post by Atlatl Axolotl Post by duke Post by Teresita Post by duke
Because it's not rotating about it's cg unless maybe you pulling the ball on the
ground. The moon, sans string, is under independent rotation as it circles the
So if we attached a string to the moon, would it then immediately wind
down the string and crash into the Earth, since you affirm it is rotating?
With the same face of the moon always facing the earth, the critical attachment
becomes the string to the earth.
.> >If the same face of the Moon always faces the Earth, how does that make
.> >it different from a ball on a string, when the point where the ball is
.> >attached to the string always faces the person who is spinning it?
.> Good grief, but you're ignorant.
Translating that from Earlspeak (why do I suddenly feel like Garrett Morris?)
"I have no answer for that. So may I interest you in an insult instead?"
If Duke claimed that the Moon did NOT rotate, then I'd understand that
he was using a reference frame that was rotating, and he'd have a
(astonishingly weak) case. But he asserts the Moon DOES rotate, and a
ball on a string does NOT. And I'm the ignorant one, he says.
I was going to write a little joke here about Euclidean geometry,
non-Euclidean according to Lovecraft, and Weberian geometry. So I
wanted to get my facts straight and I googled "types of geometry".
I'd never even heard of taxicab geometry, and I didn't know if anyone
else here had, but check this out:
"Taxicab geometry can be used to assess the differences in discrete
frequency distributions. For example, in RNA splicing positional
distributions of hexamers, which plot the probability of each hexamer
appearing at each given nucleotide near a splice site, can be compared
with L1-distance. Each position distribution can be represented as a
vector where each entry represents the likelihood of the hexamer
starting at a certain nucleotide. A large L1-distance between the two
vectors indicates a significant difference in the nature of the
distributions while a small distance denotes similarly shaped
distributions. This is equivalent to measuring the area between the
two distribution curves because the area of each segment is the
absolute difference between the two curves' likelihoods at that point.
When summed together for all segments, it provides the same measure as
I know it's geeky, but it's pretty damn cool regardless.
"If God listened to the prayers of men, all men would quickly have
perished: for they are forever praying for evil against one another."