When is breeding for drought tolerance optimal if drought is random?
Bibliographic record
Abstract
* Increasing climatic unpredictability associated with characteristics of some species makes plant drought-tolerance an important drought-adaptation strategy. Using norm-of-reaction functions, or empirically determined functions that enable us to predict the state of a trait given the state of an environmental variable, allows modelling of plant performance when water availability varies randomly. * A mathematical model is proposed to evaluate drought-tolerance and growth strategies given a set of environmental parameters: the frequency of rainy days, the soil water-storage capacity, plant water use and plant growth rates. This model compares the performance of genotypes that differ in drought tolerance expressed as the ability to grow in drier soils, and assumes a general trade-off function between drought tolerance and maximum plant growth rate. * It is worth selecting plants with a greater degree of drought tolerance, expressed by the ability to grow in drier soils whenever the frequency of rains is smaller than the rate of soil water depletion. Otherwise, maximizing growth rate at the expense of drought tolerance is the best strategy. The nature of the trade-off between drought tolerance and plant growth rate also constrains the selection for optimal drought-adapted genotypes. * Breeders will have to consider these aspects of plant-environment interactions before establishing selection programs for drought adaptation.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".