Soil CO<sub>2</sub> efflux in contrasting boreal deciduous and coniferous stands and its contribution to the ecosystem carbon balance
Bibliographic record
Abstract
Abstract Similar nonsteady‐state automated chamber systems were used to measure and partition soil CO2 efflux in contrasting deciduous (trembling aspen) and coniferous (black spruce and jack pine) stands located within 100 km of each other near the southern edge of the Boreal forest in Canada. The stands were exposed to similar climate forcing in 2003, including marked seasonal variations in soil water availability, which provided a unique opportunity to investigate the influence of climate and stand characteristics on soil CO2 efflux and to quantify its contribution to the net ecosystem CO2 exchange (NEE) as measured with the eddy‐covariance technique. Partitioning of soil CO2 efflux between soil respiration (including forest‐floor vegetation) and forest‐floor photosynthesis showed that short‐ and long‐term temporal variations of soil CO2 efflux were related to the influence of (1) soil temperature and water content on soil respiration and (2) below‐canopy light availability, plant water status and forest‐floor plant species composition on forest‐floor photosynthesis. Overall, the three stands were weak to moderate sinks for CO2 in 2003 (NEE of −103, −80 and −28 g C m−2 yr−1 for aspen, black spruce and jack pine, respectively). Forest‐floor respiration accounted for 86%, 73% and 75% of annual ecosystem respiration, in the three respective stands, while forest‐floor photosynthesis contributed to 11% and 14% of annual gross ecosystem photosynthesis in the black spruce and jack pine stands, respectively. The results emphasize the need to perform concomitant measurements of NEE and soil CO2 efflux at longer time scales in different ecosystems in order to better understand the impacts of future interannual climate variability and vegetation dynamics associated with climate change on each component of the carbon balance.
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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 | 0.000 |
| 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".