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
The combination of finite-difference approximations to the derivatives and the use of a high speed digital computer leads to a very powerful approach to solving the nonlinear ordinary and partial differential equations of physics. For many nonlinear systems, particularly those where the nonlinear terms are not small corrections to an otherwise linear behavior, the numerical route may be the best or only feasible way to travel. For the nonlinear ODEs encountered earlier in the text, the student has been allowed to use the Mathematica numerical ODE solver without any explanation provided of the principles on which it is based. In this chapter, we would like to partially fill that void by briefly describing how some of the common numerical schemes for solving nonlinear ODEs are derived. Our aim is to provide a simple conceptual framework that will make the reader more comfortable with the numerical approach while progressing through the rest of the topics that lie ahead. It should be emphasized that we are not attempting to explain the code which underlies Mathematica’s NDSolve command which is about 500 pages long.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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