Bibliographic record
Abstract
We show how to use Maple as a source language for meta-programming, with C being the target language, for experimenting with and solving combinatorial problems. We will illustrate this approach with toy problems, as well as with more substantial projects. In the latter case, familiarity with basic concepts from compiler theory may be advantageous to the reader. One advantage of using Maple as a source language for meta-programming for combinatorial problems, lies in the fact that we can use Maple’s symbolic engine to perform complicated manipulations of mathematical objects fast and reliably and therefore produce easily bug-free code in the target language. Another advantage is that we can use Maple’s underlying powerful programming language, including functions, procedures and modules, to create a meta-program that is easy to debug, modify and maintain. The target language can be changed to any other language the user is acquainted and/or at ease with, for example Java, Perl, Python, MPI and so forth. The approach we advocate can be used for both educational and research purposes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".