Bibliographic record
Abstract
This book focuses on defining, understanding, and fitting differential equation models to data. It is intended for undergraduate students and includes extensive end-of-chapter exercises, making it suitable as a course textbook. A clear, informal style is used throughout the book, with examples primarily drawn from the biological sciences. The basics of R are covered in Chapter 2, and R code is incorporated throughout the text. The author has placed various fitting and visualisation functions in the demodelr R package, which is available on the Comprehensive R Archive Network. There is a free online version of this book available at: https://jmzobitz.github.io/ModelingWithR/. The book is divided into 27 short chapters and provides an introduction to a variety of topics but does not go into detail on any of them. While there is sufficient information to stimulate interest in a number of topics, it does not provide enough coverage to become competent, so it cannot be used as a practical guide for fitting such models to data. In particular, a discussion of model diagnostics (residuals, leverage, influential observations, etc.) is omitted. For the intended audience, however, this omission can be excused.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.044 | 0.028 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".