Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it