Linking structure to function: the connection between mesophyll structure and intrinsic water use efficiency
Bibliographic record
Abstract
Climate change-driven drought events are becoming unescapable in an increasing number of areas worldwide. Understanding how plants are able to adapt to these changing environmental conditions is a non-trivial challenge. Physiologically, improving a plant's intrinsic water use efficiency (WUEi ) will be essential for plant survival in dry conditions. Physically, plant adaptation and acclimatisation are constrained by a plant's anatomy. In other words, there is a strong link between anatomical structure and physiological function. Former research predominantly focused on using 2D anatomical measurements to approximate 3D structures based on the assumption of ideal shapes, such as spherical spongy mesophyll cells. As a result of increasing progress in 3D imaging technology, the validity of these assumptions is being assessed, and recent research has indicated that these approximations can contain significant errors. We suggest to invert the workflow and use the less common 3D assessments to provide corrections and functions for the more widely available 2D assessments. By combining these 3D and corrected 2D anatomical assessments with physiological measurements of WUEi , our understanding of how a plant's physical adaptation affects its function will increase and greatly improve our ability to assess plant survival.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".