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
Let a be a polynomial in Z[x 1 , ... , x n ] that is represented by a black box. In this thesis, we have designed and implemented a new factorization algorithm that, on input of the black box, outputs the irreducible factors of a in the sparse representation. Our new algorithm based on sparse Hensel lifting applies equally well to general multivariate polynomials, both sparse and dense. We first designed the algorithm for a being monic in x 1 and square-free, then completed the factorization problem by considering a being non-monic, non-square-free, and non-primitive. Our algorithm first finds the factors of the primitive part of a , then the factors of the content of a in the main variable x 1 . We implemented our algorithm in Maple with some subroutines in C. A variety of timing benchmarks are presented. All our timings are much faster than the current best determinant and factorization algorithms in Maple and Magma. We also present a worst-case complexity analysis of our new black box factorization algorithm, along with a failure probability analysis. The case for large integer coefficients has also been considered.
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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| 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