Optimal stomatal theory predicts <scp>CO<sub>2</sub></scp> responses of stomatal conductance in both gymnosperm and angiosperm trees
Bibliographic record
Abstract
Summary Optimal stomatal theory predicts that stomata operate to maximise photosynthesis (Anet) and minimise transpirational water loss to achieve optimal intrinsic water‐use efficiency (iWUE). We tested whether this theory can predict stomatal responses to elevated atmospheric CO2 (eCO2), and whether it can capture differences in responsiveness among woody plant functional types (PFTs). We conducted a meta‐analysis of tree studies of the effect of eCO2 on iWUE and its components Anet and stomatal conductance (gs). We compared three PFTs, using the unified stomatal optimisation (USO) model to account for confounding effects of leaf–air vapour pressure difference (D). We expected smaller gs, but greater Anet, responses to eCO2 in gymnosperms compared with angiosperm PFTs. We found that iWUE increased in proportion to increasing eCO2 in all PFTs, and that increases in Anet had stronger effects than reductions in gs. The USO model correctly captured stomatal behaviour with eCO2 across most datasets. The chief difference among PFTs was a lower stomatal slope parameter (g1) for the gymnosperm, compared with angiosperm, species. Land surface models can use the USO model to describe stomatal behaviour under changing atmospheric CO2 conditions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".