Bibliographic record
Abstract
Elevated atmospheric CO2 concentration is expected to increase leaf CO2 assimilation rates, thus promoting plant growth and increasing leaf area. It also decreases stomatal conductance, allowing water savings that have been hypothesized to drive large-scale greening, in particular in arid and semiarid climates. However, the increase in leaf area could reduce the ameliorating effect of elevated CO2 concentration on soil water depletion. The net effect of elevated CO2 on leaf- and canopy-level gas exchange thus remains unclear. To address this question, a heuristic model based on the Partitioning of Equilibrium Transpiration and Assimilation (PETA) hypothesis and a model based on stomatal optimization theory are used and their outcomes compared. Predicted relative changes in leaf- and canopy-level gas exchange rates are used as a metric of responses to changes in atmospheric CO2 concentration. Both models predict reductions of leaf-level transpiration rate due to decreased stomatal conductance under elevated CO2, but negligible (PETA) or no (optimization) changes in canopy-level transpiration due to the compensatory effect of increased leaf area. Leaf- and canopy-level CO2 assimilation are predicted to increase, with an amplification of the CO2 fertilization effect at the canopy-level due to the enhanced leaf area. The expected increase in vapor pressure deficit (VPD) under warmer conditions is predicted to decrease the sensitivity of gas exchange to atmospheric CO2 concentration in both models except at growth temperatures lower than the photosynthetic thermal optimum. The consistent predictions by different models that canopy-level transpiration varies little under elevated CO2 due to combined stomatal conductance reduction and leaf area increase highlights the coordination of physiological and morphological characteristics in vegetation to maximize resource use (here water) under altered atmospheric 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.048 | 0.025 |
| Insufficient payload (model declined to judge) | 0.033 | 0.041 |
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".