Bibliographic record
Abstract
By far the greater part of biological science, quantitatively, is devoted to animals, because we are among them, and more specifically because the study of them may advance medicine. There are two great differences between animals and plants from the viewpoint of how they develop. First, animals have a precise body plan, the formation of which must be completely executed before they can properly function. Second, in their developmental processes, cells move, changing their relative positions sometimes individually and sometimes by concerted streaming of large numbers of cells together. Both of these differences make experimental pursuit of the concepts of pattern formation more difficult in animals than in plants. The constancy of numbers of parts, such as limbs, well regulated in the face of varying embryo size, is a challenge to theory, because it shows that developmental mechanisms can do more than mark out a scale of distances, but can also adjust that scale to embryo size. For plants, I have shown by a number of examples in Part I that quantitative spatial measurements during development are very valuable in trying to correlate theory and experiment. I discuss spatial measurements in relation to Drosophila segmentation in Chapter 8; but I think that any detailed account of the phenomenon of gastrulation, which I discuss briefly in Section 9.4, will show the reader that it would be very difficult to devise a programme of quantitative spatial measurement to study the applicability of particular theories there.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".