Landscape heterogeneity, soil climate, and carbon exchange in a boreal black spruce forest
Bibliographic record
Abstract
We measured soil climate and the turbulent fluxes of CO2, H2O, heat, and momentum on short towers (2 m) in a 160-yr-old boreal black spruce forest in Manitoba, Canada. Two distinct land cover types were studied: a Sphagnum-dominated wetland, and a feathermoss (Pleurozium and Hylocomium)-dominated upland, both lying within the footprint of a 30-m tower, which has measured whole-forest carbon exchange since 1994. Peak summertime uptake of CO2, was higher in the wetland than for the forest as a whole due to the influence of deciduous shrubs. Soil respiration rates in the wetland were approximately three times larger than in upland soils, and 30% greater than the mean of the whole forest, reflecting decomposition of soil organic matter. Soil respiration rates in the wetland were regulated by soil temperature, which was in turn influenced by water table depth through effects on soil heat capacity and conductivity. Warmer soil temperatures and deeper water tables favored increased heterotrophic respiration. Wetland drainage was limited by frost during the first half of the growing season, leading to high, perched water tables, cool soil temperatures, and much lower respiration rates than observed later in the growing season. Whole-forest evapotranspiration increased as water tables dropped, suggesting that photosynthesis in this forest was rarely subject to water stress. Our data indicate positive feedback between soil temperature, seasonal thawing, heterotrophic respiration, and evapotranspiration. As a result, climate warming could cause covariant changes in soil temperature and water table depths that may stimulate photosynthesis and strongly promote efflux of CO2 from peat soils in boreal wetlands.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".