Comparing the carbon budgets of boreal and temperate deciduous forest stands
Bibliographic record
Abstract
Boreal and temperate deciduous forests at northern mid-latitudes play an important role in the global carbon cycle. We analyze 3 years (1996-1998) of eddy-covariance carbon dioxide flux measurements from two contrasting deciduous forest ecosystems in the boreal and temperate regions of central Canada. The two forest stands have similar ages, heights, and leaf area indices but differ in species composition and climate. Mean annual net ecosystem productivity (NEP) was similar for the two ecosystems, varying between 0.7 and 2.7 t C·ha1 (boreal) and 0.6 and 2.4 t C·ha1 (temperate). In the boreal ecosystem, interannual differences in NEP were primarily controlled by early spring temperature. The warm spring of 1998 caused early leaf out and increased photosynthesis but had little effect on respiration. In the temperate ecosystem, the same warm spring not only caused early leaf out but also increased respiration and drought stress. The contrasting impact of the warm spring on annual NEP at the two sites illustrates the complexity of interpreting climatic impacts on the forest carbon balance. It also illustrates two competing influences of climate change on NEP: spring warming, which promotes photosynthesis and increases NEP, and increased soil temperature and drought, which promote respiration and reduce photosynthesis, thus reducing NEP. We discuss the need for a consistent data post-processing methodology in ecosystem intercomparisons. We also compare our results with a recent synthesis of data from European 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".