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
Welcome to the third issue of Maple Transactions. For a variety of global reasons, this issue's production was slow enough that by the time the original contributions were ready, a whole new batch of contributions were also ready. So this is basically a "double issue". We have two Featured Contributions: "How to Hunt Wild Constants" which surveys the software for guessing what a floating-point constant might really be; and another on "Arbitrary precision computation of the gamma function" which surveys the state-of-the-art for computation of that remarkable function. We have our first video presentation hosted on Western's Institutional Repository instead of YouTube, for better international access. We have contributions on mathematical research and on educational research, and on educational practice. We have a nice educational paper on how to program in Maple. And, since it's kind of a double issue, I have written two columns for the Editor's corner. I hope you will enjoy them, but more than that I am certain that you will find a lot of interesting material in this issue.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.006 |
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