Bibliographic record
Abstract
The principal aim of this book is to emphasize the geometric structure of Continuum Mechanics, but a reader not familiar with the by now standard presentation of this discipline is likely to miss the punch line. In part to avoid this unintended situation and in part to have a basic conceptual and terminological framework for the rest of the book, in this Appendix we provide a concise presentation of the subject as it can be found in more or less standard elementary textbooks. The level of mathematical sophistication is kept as low as possible: vectors are arrows, Pythagoras reigns supreme, everything is nice and smooth. Bodies and Configurations The passage from the classical mechanics of finite systems of particles and rigid bodies to the mechanics of deformable continua is not a trivial one. Already at the beginning of the subject we find ourselves confronted with the problem of defining the main concept: the body, or material continuum. Is it merely an infinite collection of particles? Because we must, at the very least, be able to define fields (temperature, velocity, and so on) over this entity, it is clear that we need a rigorous definition. This need is all the more pressing since, from our experience with centuries-old particular theories (hydrodynamics, linear elasticity, and so on), we know that soon enough temporal and spatial derivatives of these fields will enter the scene.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.040 | 0.021 |
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".