This is a problem that machine learning can tackle. Feel free to contact me by PM for technical help.
To make sure I understand your problem:
We have many copies of the Big Book. Each copy is a collection of many sheets. Each sheet was produced by a single tool, but each tool produces many sheets. Each shop contains many tools, but each tool is owned by only one shop.
Each sheet has information in the form of marks. Sheets created by the same tool at similar times have similar marks. It may be the case that the marks monotonically increase until the tool is repaired.
Right now, we have enough to take a database of marks on sheets and figure out how many tools we think there were, how likely it is each sheet came from each potential tool, and to cluster tools into likely shops. (Note that a 'tool' here is probably only one repair cycle of an actual tool, if they are able to repair it all the way to freshness.)
We can either do this unsupervised, and then compare to whatever other information we can find (if we have a subcollection of sheets with known origins, we can see how well the estimated probabilities did), or we can try to include that information for supervised learning.
This is a problem that machine learning can tackle. Feel free to contact me by PM for technical help.
Good point!
Also yay combining multiple fields of knowledge and expertise! applause
Seriously though, the world does need more of it, and I felt the need to explicitly reward and encourage this.
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A list of some posts that are pretty awesome
I recommend the major sequences to everybody, but I realize how daunting they look at first. So for purposes of immediate gratification, the following posts are particularly interesting/illuminating/provocative and don't require any previous reading:
More suggestions are welcome! Or just check out the top-rated posts from the history of Less Wrong. Most posts at +50 or more are well worth your time.
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