Seasonal controls of canopy chlorophyll content on forest carbon uptake: Implications for GPP modeling
Bibliographic record
Abstract
Abstract Forested ecosystems represent an important part of the global carbon cycle, with accurate estimates of gross primary productivity (GPP) crucial for understanding ecosystem response to environmental controls and improving global carbon models. This research investigated the relationships between leaf area index (LAI) and leaf chlorophyll content (ChlLeaf) with forest carbon uptake. Ground measurements of LAI and ChlLeaf were taken approximately every 9 days across the 2013 growing season from day of year (DOY) 130 to 290 at Borden Forest, Ontario. These biophysical measurements were supported by on‐site eddy covariance flux measurements. Differences in the temporal development of LAI and ChlLeaf were considerable, with LAI reaching maximum values within approximately 10 days of bud burst at DOY 141. In contrast, ChlLeaf accumulation only reached maximum values at DOY 182. This divergence has important implications for GPP models which use LAI to represent the fraction of light absorbed by a canopy (fraction of absorbed photosynthetic active radiation (fAPAR)). Daily GPP values showed the strongest relationship with canopy chlorophyll content (ChlCanopy; R2 = 0.69, p < 0.001), with the LAI and GPP relationship displaying nonlinearity at the start and end of the growing season (R2 = 0.55, p < 0.001). Modeled GPP derived from LAI × PAR and ChlCanopy × PAR was tested against measured GPP, giving R2 = 0.63, p < 0.001 and R2 = 0.82, p < 0.001, respectively. This work demonstrates the importance of considering canopy pigment status in deciduous forests, with models that use fAPARLAI rather than fAPARChl neglecting to account for the importance of leaf photosynthetic potential.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".