Leaf optical responses to light and soil nutrient availability in temperate deciduous trees
Bibliographic record
Abstract
Leaf optical parameters influence light availability at the cellular, leaf, and canopy scale of integration. While recent studies have focused on leaf optical responses to acute plant stress, the effects of changes in plant resources on leaf optics remain poorly characterized. We examined leaf optical and anatomical responses of five temperate deciduous tree species to moderate changes in nutrient and light availability. Spectral reflectance in the visible waveband generally increased at high light, but decreased with increased nutrient availability. Patterns of both spectral reflectance and absorptance were primarily determined by chlorophyll concentration although carotenoid concentration was also influential. While most anatomical features did not explain residual variation in reflectance, cuticle thickness was significantly related to reflectance at complementary angles compared to the angle of incidence. Absorptance did not change with light environment; however, absorption efficiency per unit biomass increased by approximately 40% under low light, due to reduced leaf mass per area. We conclude that changes in resource availability differentially influence leaf optical properties and that such changes are driven primarily by changes in pigment concentrations. The magnitude of leaf optical responses to moderate changes in resource availability was comparable to those of acute stress responses and varied among species.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".