Interspecific variation in the temperature response of mesophyll conductance is related to leaf anatomy
Bibliographic record
Abstract
SUMMARY Although mesophyll conductance (gm) is known to be sensitive to temperature (T), the mechanisms underlying the temperature response of gm are not fully understood. In particular, it has yet to be established whether interspecific variation in gm–T relationships is associated with mesophyll anatomy and vein traits. In the present study, we measured the short‐term response of gm in eight crop species, and leaf water potential (Ψleaf) in five crop species over a temperature range of 15–35°C. The considered structural parameters are surface areas of mesophyll cells and chloroplasts facing intercellular airspaces per unit leaf area (Sm and Sc), cell wall thickness (Tcw), and vein length per area (VLA). We detected large interspecific variations in the temperature responses of gm and Ψleaf. The activation energy for gm (Ea,gm) was found to be positively correlated with Sc, although it showed no correlation with Tcw. In contrast, VLA was positively correlated with the slope of the linear model of Ψleaf–T (a), whereas Ea,gm was marginally correlated with VLA and a. A two‐component model was subsequently used to model gm–T relationships, and the mechanisms underlying the temperature response of gm are discussed. The data presented here indicate that leaf anatomy is a major determinant of the interspecific variation in gm–T relationships.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".