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
Gradient descent methods for large positive definite linear and nonlinear algebraic systems arise when integrating a PDE to steady state and when regularizing inverse problems. However, these methods may converge very slowly when utilizing a constant step size, or when employing an exact line search at each step, with the iteration count growing proportionally to the condition number. Faster gradient descent methods must occasionally resort to significantly larger step sizes, which in turn yields a strongly nonmonotone decrease pattern in the residual vector norm.In fact, such faster gradient descent methods generate chaotic dynamical systems for the normalized residual vectors. Very little is required to generate chaos here: simply damping steepest descent by a constant factor close to 1 will do. The fastest practical methods of this family in general appear to be the chaotic, two-step ones. Despite their erratic behavior, these methods may also be used as smoothers, or regularization operators. Our results also highlight the need for better theory for these methods.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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