Conservatives and Gamblers: Interpreting plant functional response to water stress in terms of a single indicator
Bibliographic record
Abstract
Water availability has a decisive impact on plant growth, survival and distribution. Climate change is expected to alter both the amount and variability of precipitation. To predict and understand plant responses to water stress, efficient and robust mechanisms for describing their functional responses to water availability are needed. However, most ecohydrological processes and models which take into account these responses are inherently complex, difficult to understand and require large amounts of data. We develop a novel straightforward approach and hypothesize that: (1) Plants exhibit two archetypical response patterns under water stress, one typical to slow-growing plants and one typical to fast-growing ones, with most plants being situated between these two. (2) Differences within and between these functional types can be adequately described by a single parameter - the threshold of relative soil water content - at which plants reduce their transpiration in response to water stress. This indicator is straightforward and relies on data which is relatively easy to measure. Its effect has been previously described and it is already used in several models to simulate the effect of water stress on plants. In our approach, we combine this indicator with a description of reactions patterns. This combination provides a general and efficient way of classifying plant responses and allows the assessment of the impact of water stress on a wide variety of plants. Due to its simplicity, our approach offers the opportunity to include water relations of plants in a larger set of models and descriptions than it is possible with more complex ecohydrological descriptions. It also can be used to explain biodiversity in fluctuating environments.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".