Foliar nitrogen in relation to plant traits and reflectance properties of New Hampshire forests
Bibliographic record
Abstract
Several recent studies have shown that the mass-based concentration of nitrogen in foliage (%N) is positively correlated with canopy near-infrared reflectance (NIRr) and midsummer shortwave albedo across North American forests. Understanding the mechanisms behind this relationship would aid in interpretation of remote sensing imagery and improve our ability to predict changes in reflectance under future environmental conditions. The purpose of this study was to investigate the extent to which foliar nitrogen at leaf and canopy scales covary with leaf- and canopy-scale structural traits that are known to influence NIR scattering and reflectance. To accomplish this, we compared leaf and canopy traits with reflectance spectra at 17 mixed temperate forest stands. We found significant positive associations among %N and NIRr at both the leaf and canopy scale. At the canopy scale, both %N and NIRr were correlated with a number of structural traits as well as with the proportional abundance of deciduous and evergreen foliage. Identifying specific causal factors for observed reflectance patterns was complicated by interrelations among multiple traits across scales. Among simple metrics of canopy structure, we saw no relationship between NIRr and leaf area index, but we observed a strong, inverse relationship with the number of leaves per unit canopy volume.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".