I have had experiences like this several times also so it is interesting to look at an example from someone else.
Probing it about CenPCA it gave these two definitions:
"CenPCA is an abbreviation for Centralized Principal Component Analysis. It is a dimensionality reduction technique used in data analysis for compressing high-dimensional data into a lower-dimensional representation. The centralization step of CenPCA involves centering the data, making the first principal component aligned with the mean of the data. This makes it possible to capture the overall structure of the data and to highlight the most important patterns in the data set. CenPCA has been used in various applications such as finance, image processing, and bioinformatics to analyze large and complex datasets."
"CenPCA refers to "Centered PCA", which is a variant of Principal Component Analysis (PCA), a widely used method in machine learning and data analysis. The key difference between regular PCA and centered PCA is that in centered PCA, the data is first centered around the mean before performing the PCA, while in regular PCA, the mean is not subtracted from the data. This has implications in the interpretation of the results, as centered PCA can sometimes provide more meaningful principal components."
I would think there has to be something to this but it is confusing exact titles although I have had it make up python code that simply is nonsense also.
Another type of example I had was during a language philosophy discussion it recommended the paper:
"Language Evolution: The Hard Problems" by Morten H. Christiansen and Simon Kirby is a paper that explores some of the challenges and limitations of current theories of language evolution."
It was actually just a book called "Language Evolution" by Morten H. Christiansen and Simon Kirby but trying to find that exact paper title made me think it made it up.
I am tending towards becoming addicted to it still after massive frustration with it for things like this at first. It seems you just have to be careful when probing the outer edges of knowledge and to not get led down these nonsense paths.