Resolving the contrasting leaf hydraulic adaptation of <scp>C<sub>3</sub></scp> and <scp>C<sub>4</sub></scp> grasses
Bibliographic record
Abstract
Summary Grasses are exceptionally productive, yet their hydraulic adaptation is paradoxical. Among C 3 grasses, a high photosynthetic rate ( A area ) may depend on higher vein density ( D v ) and hydraulic conductance ( K leaf ). However, the higher D v of C 4 grasses suggests a hydraulic surplus, given their reduced need for high K leaf resulting from lower stomatal conductance ( g s ). Combining hydraulic and photosynthetic physiological data for diverse common garden C 3 and C 4 species with data for 332 species from the published literature, and mechanistic modeling, we validated a framework for linkages of photosynthesis with hydraulic transport, anatomy, and adaptation to aridity. C 3 and C 4 grasses had similar K leaf in our common garden, but C 4 grasses had higher K leaf than C 3 species in our meta‐analysis. Variation in K leaf depended on outside‐xylem pathways. C 4 grasses have high K leaf : g s , which modeling shows is essential to achieve their photosynthetic advantage. Across C 3 grasses, higher A area was associated with higher K leaf , and adaptation to aridity, whereas for C 4 species, adaptation to aridity was associated with higher K leaf : g s . These associations are consistent with adaptation for stress avoidance. Hydraulic traits are a critical element of evolutionary and ecological success in C 3 and C 4 grasses and are crucial avenues for crop design and ecological forecasting.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".