Environmental control of leaf area production: Implications for vegetation and land‐surface modeling
Bibliographic record
Abstract
Leaf surface area per unit ground cover (leaf area index, LAI) is one of the major controls on plant productivity and biospheric feedbacks on atmospheric energy and water exchanges. Nearly all vegetation and land‐surface models include parameterizations of LAI, however not much research currently focuses on the validation of simulated responses of LAI to environmental change. The objective of our research was to quantitatively review the plant science literature to extract information on the response of LAI to variations in soil moisture, soil fertility and atmospheric CO2. Our synthesis confirms that LAI is likely co‐limited by a number of resources, including water, nitrogen and light. Atmospheric CO2 influences LAI in much the same manner as other plant resources. When CO2 supply is strongly limiting to gross primary production (i.e., at relatively low CO2 concentrations), LAI is strongly correlated with CO2, whereas when CO2 is abundant, LAI sensitivity to CO2 dramatically decreases. Such a nonlinear relationship between leaf area production and atmospheric CO2 may introduce a potential bias for global change modeling, particularly in the simulation of low‐density vegetation that has the potential to significantly increase canopy size without inducing self‐shading.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".