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
Chapter 5 starts out with a physics motivation, as well as a mathematical statement of the problems that will be tackled in later sections. Several methods are introduced to solve a single nonlinear equation in one variable: fixed-point iteration, the bisection method, Newton’s method, the secant method, and Ridders’ method. After providing some advice about advantages and disadvantages of each approach, the text then studies how to find zeros of polynomials, employing two different techniques. The sophistication is then increased, by tackling systems of nonlinear equations and examining the corresponding challenges; in addition to Newton’s method, the text derives the equations behind Broyden’s method. A related subject is then broached, minimization in one or several dimensions; this includes the gradient-descent method, as well as detailed analysis of critical points; the second edition includes extensive new material on derivative-free optimization (golden-section search and Powell’s method).The chapter is rounded out by a physics project, the extremization of the action in classical mechanics, and a problem set. The physics project shows Hamilton’s principle in... action, translated into a multidimensional minimization problem.
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 | 0.001 |
| 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