Bibliographic record
Abstract
Lionel's text, to this point, brings us to 2008. Some further comments are therefore useful, both to round out Lionel's thoughts, and to relate his ideas more fully to current research in the field, now a rapidly expanding one. Lionel's unique voice is apparent throughout the book. It is not intended as a guide book to quantitative work in development – Biological Physics of the Developing Embryo by Forgacs and Newman (2005) is a good example of this. Nor is it a broad survey of pattern-formation processes and how they apply in biology – Philip Ball's Nature's Patterns (2009) is exemplary in that regard. Rather, the focus is on how one goes about exploring and testing the potential of a particular theory, developed in this case first by Turing and extended since by others; it is as much a commentary on how to construct a quantitative biology as it is an examination of particular dynamic issues in development. Lionel began his work in biology in the early 1970s, and was part of the blossoming of ideas in self-organization and how they might apply in biology. As chronicled here, these ideas gained a degree of acceptance by a segment of the developmental community, despite the division of cultures Lionel has described. The 1990s, though, heralded increasingly powerful techniques for manipulating gene regulation, with an increasingly detailed mapping of the components of developmental pathways, and little emphasis on overall dynamic constraints for patterning.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.418 | 0.234 |
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".