Great question - the "universe" of venues we address is centered on peer-reviewed machine learning like ICML, ICLR, NeurIPS, COLM because (a) we have expertise in this area and so can therefore judge the quality and (b) it's all open access.

Therefore, the further you get away from the topic of ML, the less dense our coverage. Frankly, it's a tough to editorial choice to determine which research directions and therefore which proposals to include. ML is quite multi-disciplinary. And people who have a few papers in ML but many in (e.g.) medicine get upset when their profile is sparse. But we can't possibly produce good proposals in medicine due to its tendency to be closed-access.

Thanks; there is PMC, though it’s possible you may have already used it:

https://pmc.ncbi.nlm.nih.gov/

Hmm, interesting. Yea I think ML is a much smaller field, too. ArXiv + all the conference / applied ml stuff is ~150-250k papers per year. PubMed is 1.5-1.7 million new records per year, and like 40,000,000 total records, whereas in ML we only had to process a couple million. Plus, pretty much all of ML is open-access, whereas medical is ~50% closed.

If somebody gave us a $10-$20m grant for compute, and a few (dozen) helpful doctors showed up to help us QA / figure out how to approach it, it could probably be done.

Thank you!

One part of the explanation is that we focus primarily on AI research, and a substantial chunk of physicists gravitate (heh) towards AI research, so we end up capturing more of their work.

Another part of the explanation could be that (AI +) physics papers are just easier to access, so we end up processing more of those.