Here’s the paper, published only recently: https://royalsocietypublishing.org/doi/10.1098/rsos.200566

I bet you could do some ML to predict replicability of a paper (per author's suggestion that it's laughably easy to predict)

I am betting any such ML system could be gamed and addressing the issue would ultimately still need humans in the loop. For example, what if I am selective with my data, beyond the visibility of ML evaluating the final published paper? I don’t think this is “laughably easy” to predict. It may be easy to spot telltale signs today that predict replicability, but as soon as those markers are understood, I imagine authors will simply squeeze papers through the cracks in a different way.

Another issue is this bit from the author on Twitter:

Just because it replicates doesn't mean it's good. A replication of a badly designed study is still badly designed. There are tons of papers doing correlational analyses yet drawing causal conclusions, and many of them will successfully replicate. Doesn't mean they're justified.