It's a fair point for standard software engineering libraries, that the defaults should be obvious, but I don't think that it's possible to hold scientific libraries to the same standards. They require expert knowledge, and the assumptions you make about the models that are being used should be very carefully checked. There is no obvious defaults in that case because we're not writing software, we're building scientific models.
For example, look at the neural network classifier model in scikit-learn:
https://scikit-learn.org/stable/modules/generated/sklearn.ne...
None of these defaults can be assumed. If you showed me a function call with no optional arguments I couldn't tell you, nor anybody else that hasn't read the documentation, what activation is being used, what optimizer, learning rate, etc...
In fact when coming across these kind of models during code reviews the nature of these parameters is the first thing I ask about.
For example, look at the neural network classifier model in scikit-learn: https://scikit-learn.org/stable/modules/generated/sklearn.ne... None of these defaults can be assumed. If you showed me a function call with no optional arguments I couldn't tell you, nor anybody else that hasn't read the documentation, what activation is being used, what optimizer, learning rate, etc... In fact when coming across these kind of models during code reviews the nature of these parameters is the first thing I ask about.