Winter Soil Respiration, Temperature, and Soil Moisture in Snow-Manipulated Postfire and Undisturbed Black Pine Forests in Taşköprü, Kastamonu District, Türkiye
Bibliographic record
Abstract
Climate change has been fueling forest fires worldwide and driving warmer winters in temperate countries. Recently, wildfires devastated large track of forest lands in Canada and Hawaii, resulting in the unfortunate loss of lives and properties. A warmer climate forecast not only increases the occurrence of forest fires but also brings about a snow-free warm winter season. Given these consequences of a warmer climate, a critical question is whether these climate change-related impacts significantly enhance soil respiration (Rs) in forest fire areas and standing undisturbed forest ecosystems. We conducted a field experiment in one of the recently burned black pine forest (Pinus nigra Arnold). We employed an automated soil respiration machinery (Li-8100A, LiCor BioSciences) to measure the soil CO2 emissions, soil temperature, air temperature, and soil moisture simultaneously. We found that a warmer winter results in higher soil respiration rates and warmer soil temperatures in undisturbed forests, indicating its less sensitivity to snow cover. In contrast, the snow-free post-fire treatment exhibited significantly reduced soil respiration rates and freezing soil temperature at the height of the winter season. We concluded that the complementary effects of lack of snow and forest fire resulted in a significant decrease in soil respiration rates, and, thus, potentially resulted in the conservation of soil C stocks during the winter period. The higher soil respiration and warmer soil in the undisturbed forest could accelerate the decomposition of soil organic matter and increase the contribution to atmospheric CO2, thus providing positive feedback to climate change. Given the global concerns about climate change impacts and the frequency of forest fires, the findings of this study would help us understand the impacts of forest fires and lack of snow in climate-soil respiration feedback.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".