Bibliographic record
Abstract
INTRODUCTION Plants are exposed to unusual, even extreme, environmental conditions, daily, seasonally, or from time to time depending on where they live. Beneath the benign face of the natural green world, plants are waging battles constantly against difficulties posed by their environments. Because these stresses often lead to reduced health in plants, just as they do in animals, they are also of considerable interest to agricultural scientists. Stressed crops usually produce lower yields. Understanding how plants cope with and respond to environmental stresses (often called abiotic stresses to distinguish them from those caused by diseases and predators, which are biotic stresses) is, therefore, important to breeders whose job it is to develop crop varieties with resistance to stresses while maintaining high yields. WHAT IS STRESS? The word stress was used first by engineers to explain what happens when a force is applied to an object; strain is the change in the object caused by the stress. For example, an elastic band can be stressed by forcing it to expand; strain is how much the band is stretched by the force applied. Stresses and strains in the physical world can often be precisely applied and measured. DEFINING “BIOLOGICAL” STRESS AND STRAIN In a cultivated context Anything that does not allow a plant to reach its full potential is a stress which will have a consequent strain, such as lower growth or seed production.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".