dondelelcaro
Usually that's just for their version. Arxiv the version before it was accepted.
Exactly.
The general approach is to use interpretable models where you can understand how the model works and what features it uses to discriminate, but that doesn't work for all ML approaches (and even when it does our understanding is incomplete.)
Maybe not the hardest, but still challenging. Unknown biases in training data are a challenge in any experimental design. Opaque ML frequently makes them more challenging to discover.
Love the cheese name! I think he looks like more like a Muenster to me, but Colby works! https://en.m.wikipedia.org/wiki/Muenster_cheese
This is why the incremental cost of a unit are often a better measure for longer term profitability and decision making than the unit average cost, especially when you aren't factoring in the market size and ability to repurpose sunk costs in that unit average cost.
Unless you're in Tibet, Xinjiang, or another place observing UTC+8 with a significant offset from local solar time.
rRNA: typical. I do the work and everyone else takes credit.
You can have debt utilization while still paying off the full statement balance each month and not being charged interest. I always have a balance, but I rarely carry the balance beyond the statement due date and interest free grace period. (I just have new charges that make the balance non zero.)
I wonder if there will be an anti SLAPP action soon from Cohen's team. (Not sure what the rules are in that jurisdiction.)
Figuring out the Parkinson's linkage is challenging too, because glyphosate is just one of many chemicals used in agricultural settings. It wouldn't be surprising for the correlation to be caused by another chemical with strong evidence of casual linkage to Parkinson's that itself is correlated with glyphosate, like Parquat. (Since Parquat is a herbicide, places that used it may also use (or have switched to) glyphosate.) Totally worth continued scientific study.