A Simple Example of a New Class of Landen Transformations
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
Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsDante MannaDANTE MANNA received his Bachelor of Arts degree in mathematics from Wesleyan University in Middle- town, Connecticut, in May 2001. He was also a Spring 2000 graduate of the Associated Colleges in China intensive Mandarin program, at Capital University in Beijing, China. At the time this article was submitted, he was a graduate student at Tulane University working under Victor Moll. He is now the AARMS Director's Postdoctoral Fellow at Dalhousie University.Victor H. MollVICTOR H. MOLL is a professor of mathematics at Tulane University. He studied under Henry McKean at the Courant Institute of New York University. After graduation in 1984, he spent two years at Temple University in Philadelphia. There he failed to pay attention when Donald J. Newman was telling him about some transformations on elliptic integrals. His research interests are in the mathematics behind the evaluation of definite integrals.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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