Integrating carbon fluxes and wood anatomical traits to unravel carbon pool partitioning using eddy covariance data, tree rings and modeling
Bibliographic record
Abstract
Boreal forest sinks one third of terrestrial carbon (C), playing a crucial role in mitigating climate change. However, our understanding of the relationship between carbon assimilation and its allocation into woody biomass production remains limited. To address this gap, we propose a novel approach that combines eddy covariance (EC), wood anatomy in tree rings, and the 3D-CMCC-FEM forest model. This integrated method aims to elucidate the pathways of C pools over short and long-time scales. The study was conducted in a boreal site of Pinus banksiana (Lamb.) in Canada, spanning from 1999 to 2019.Our results revealed notably high correlations between model-predicted and measured Gross Primary Productivity (GPP) ranging from 0.88, 0.95, 0.60 for daily, monthly, and annual scales, respectively. We observed comparable inter-annual variability between measured ring wall area (proxy of total woody biomass) and stem carbon accumulated and predicted by the model. Additionally, consistent values of carbon use efficiency (CUE = 0.41, net vs. gross primary productivity) were found when comparing modeled and estimated data in the nearby evergreen Picea mariana stand to our study site.This study represents a significant step toward enhancing our understanding of both inter-and intra-annual variability of carbon fluxes, providing insights into the pathways of C in forest —an essential challenge in estimating and projecting future carbon sink capacities of forests.
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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 | 0.001 |
| 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 | 0.000 | 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".