Bibliographic record
Abstract
In the study of forced oscillator phenomena, we have avoided plunging into heavy analysis because generally the details can be gory and are probably soon forgotten by the student. The virtue of maps, and the logistic map in particular, is that they are amenable to relatively simple, easily understandable, analysis because they are governed by finite difference equations rather than nonlinear differential equations. Despite their relative simplicity, nonlinear maps can guide us along the road to understanding many of the features that are seen in forced nonlinear oscillator systems such as the period doubling route to chaos, the stretching and folding of strange attractors, and so on. The emphasis will be on understanding rather than trying to establish the direct connection of a given map with a particular nonlinear ODE which is a nontrivial task beyond the scope of this text. Some new concepts like bifurcation diagrams and Lyapunov exponents, which will be encountered in this chapter, could have been introduced in the last chapter but are more easily dealt with in the framework of nonlinear maps.
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.
How this classification was reachedexpand
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".