Snow removal reduces annual cellulose decomposition in a riparian boreal forest
Bibliographic record
Abstract
Kreyling, J., Haei, M. and Laudon, H. 2013. Snow removal reduces annual cellulose decomposition in a riparian boreal forest. Can. J. Soil Sci. 93: 427–433. Decomposition is a key process in carbon and nutrient cycling. However, little is known about its response to altered winter soil temperature regimes in boreal forests. Here, the impact of soil frost on cellulose decomposition over 1 yr and soil biotic activity (bait-lamina sticks) over winter, in spring, and in summer was investigated using a long-term (9-yr) snow-cover manipulation experiment in a boreal Picea abies forest. The experiment consisted of the treatments: snow removal, increased insulation, and ambient control. The snow removal treatment caused longer and deeper soil frost (minimum temperature −8.6°C versus −1.4°C) at 10 cm soil depth in comparison with control, while the increased insulation treatment resulted in nearly no soil frost during winter. Annual cellulose decomposition rates were reduced by 46% in the snow removal manipulation in comparison with control conditions. Increased insulation had no significant effect on decomposition. The decomposition was mainly driven by microorganisms, as no significant difference was observed for containers enclosed with a 44-µm and a 1-mm mesh. Soil biotic activity was slightly increased by both the snow removal and the increased insulation treatment in comparison with control conditions over winter. However, this effect disappeared over spring and summer. We conclude that soil frost can have strong effects on decomposition in boreal ecosystems. Further studies should investigate to which degree the observed reduction in decomposition due to reduced snow cover in winter slows or even offsets the expected increase in decomposition rates with global warming.
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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) | 0.001 | 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".