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Found it in the Results section:

> On average, 83% of captures were consumed by the cats (88% of invertebrates, 79% of reptiles and amphibians, 78% of mammals and 50% of birds) and the remaining items were left at the site of predation.


Right. Feathers and bones don't digest well.


I think they meant the cat left 50% of birds completely alone. Not that they ate 50% of the mass of every bird. This is based on owning an outdoor cat and having it bring intact corpse trophies to the door.


For those who don't want to read the actual interesting story about chumbox advertising:

"Anyway, seems like the vegetable is corn."


Total percentages of vegetarian and vegan Americans do appear to be quite low, and they skew more liberal and lower income[1]. I'm not the person you're responding to, and it requires some extrapolation, but the liberal skew does at least suggest that vegetarians may skew "metropolitan" as well.

[1]https://news.gallup.com/poll/238328/snapshot-few-americans-v...


I appreciate what you're trying to do here, but "skew metropolitan"(your point) and "aren't common outside of certain metro areas"(op point) don't feel like the same rhetorical weight.

I mean my thoughts are the whole "Vermont Hippy" seems like a thing, and IDK that Vermont even has a meto area (/s).



That's the one! I got the details totally wrong, though :)


You weren’t that far off. Close enough that someone could find it.


I got my start by getting a PhD, but that's perhaps not a practical recommendation. In reality though, you might say I started learning ML by reading Mitchell in class: https://www.cs.cmu.edu/afs/cs.cmu.edu/user/mitchell/ftp/mlbo... It's dated, but it's quite approachable and does a great job explaining a lot of the fundamentals.

If you want to approach machine learning from a more statistical perspective, you could also have a look at An Introduction to Statistical Learning to start: http://www-bcf.usc.edu/~gareth/ISL/ Or if you're more mathematically inclined than the average bear, you could jump directly into The Elements of Statistical Learning: https://web.stanford.edu/~hastie/ElemStatLearn/

If you want something a little more interactive than a book though, you might have a look at Google's free crash course on machine learning: https://developers.google.com/machine-learning/crash-course/... I checked it out briefly maybe six months ago, and it seemed pretty good. It seemed a bit focused on Tensor Flow and some other tools, but that's okay.


Thank you very much for these resources.


Drawball


This is exactly it! Thanks.


I'm pretty sure the chickens tied together on the bike are alive. I imagine it's just a lot easier to transport a bunch of them with a bike when they're tied together like that.

https://youtu.be/MwZxBPYqiLg?t=17s


Yeah, giving it a second look, you are right.


That was a concern of mine too. It seems hard to guess how that might play out in practice though without a lot more information. For example, I wonder how localized the market effects might be. That is, how much do local populations sell chickens to one another, and how much does that affect market equilibrium.


At least one of the reviewers posted separate 5-star reviews for the paid and free version on the same day.

Edit: And on more careful inspection, they do say that they bought the paid version after liking the free version. I may have jumped the gun on that one.


Michael Pryor I think, actually.


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