Field assessments on the impact of CO <sub>2</sub> concentration fluctuations along with complex-terrain flows on the estimation of the net ecosystem exchange of temperate forests
Bibliographic record
Abstract
CO 2 storage ( F s ) is the cumulation or depletion in CO 2 amount over a period in an ecosystem. Along with the eddy covariance flux and wind-stream advection of CO 2 , it is a major term in the net ecosystem CO 2 exchange (NEE) equation. The CO 2 storage dominates the NEE equation under a stable atmospheric stratification when the equation is used for forest ecosystems over complex terrains. However, estimating F s remains challenging due to the frequent gusts and random fluctuations in boundary-layer flows that lead to tremendous difficulties in capturing the true trend of CO 2 changes for use in storage estimation from eddy covariance along with atmospheric profile techniques. Using measurements from Qingyuan Ker Towers equipped with NEE instrument systems separately covering mixed broad-leaved, oak, and larch forest towers in a mountain watershed, this study investigates gust periods and CO 2 fluctuation magnitudes and examines their impact on F s estimation in relation to the terrain complexity index (TCI). The gusts induce CO 2 fluctuations for numerous periods of 1 to 10 min over 2 h. Diurnal, seasonal, and spatial differences ( P < 0.01) in the maximum amplitude of CO 2 fluctuations ( A m ) range from 1.6 to 136.7 ppm, and these differences range from 140 to 170 s in a period ( P m ) at the same significance level. A m and P m are significantly correlated to the magnitude of and random error in F s with diurnal and seasonal differences. These correlations decrease as CO 2 averaging time windows become longer. To minimize the uncertainties in F s , a constant [CO 2 ] averaging time window for the F s estimates is not ideal. Dynamic averaging time windows and a decision-level fusion model can reduce the potential underestimation of F s by 29 %–33 % for temperate forests in complex terrain. In our study, the relative contribution of F s to the 30 min NEE observations ranged from 17 % to 82 % depending on turbulent mixing and the TCI. The study's approach is notable as it incorporates the TCI and utilizes three flux towers for replication, making the findings relevant to similar regions with a single tower.
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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 | 0.000 | 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".