Bibliographic record
Abstract
Modelling and simulation have arisen as a third branch of science alongside theory and experiment, enabling and supporting discovery, insight, prediction and action. The Information Age gave rise to an upsurge in the use of models to represent, rationalize and reason about measured and predicted appearances of the real world. This chapter describes different kinds of model—physical, mathematical, computational—and their use in different domains and for different purposes. Solutions of mathematical model equations that defied analytical method and required huge amounts of mental and manual effort for the calculations made, before the computer, became considerably more straightforward to deal with using computational methods and tools developed and refined in the Information Age. In the examples described, the focus is on pioneers I have been taught by, got to know or collaborated with: John Houghton (1931–2020) on weather and climate modelling, to give a perspective from a non-medical domain; Arthur Guyton (1919–2003) and John Dickinson (1927–2015) on modelling of body systems and clinical physiology; Louis Sheppard on model-based control systems for intensive care, and mathematical models applied to track and predict the course of epidemics and analyze clinical decisions. Other examples are from teams I have been privileged to see firsthand, as a reviewer and advisory board chair of largescale research projects across the European Union. With colleagues in the UK and Canada, I previously published the Mac Series models of clinical physiology with Oxford University Press. I have established a Cloud-based emulation environment to provide access to these working models—created in the first half of my career and thus now archaic in terms of software interface—to accompany their description in one of the chapter’s examples.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.045 | 0.016 |
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".