Different management practices impact on CO2 and H2O budget of wind disturbed forest sites- 5-year dataset
Bibliographic record
Abstract
<p>There is evidence of increasing severity of extreme meteorological events, which due to climate warming are also more frequent than in the past few decades. Any disturbances (either natural or anthropogenic) exert a significant influence on the forest’s functioning. In Canada and the USA, fires and insect outbreaks cause the greatest damage while in Europe wind disturbances are the main threat. Since in Poland the majority of forests are managed by the State Forests, after such events disturbed areas are almost immediately designated for reforestation. While natural regeneration still contributes the least to forest restoration, the most common practices in our country include harvesting, soil preparation (ploughing) and manual seedlings introduction, which in this sense is similar to clear-cut’s management. </p><p>Once such an event happened in Poland two EC stations were set up in the area of an 80-year old pine forest, which had been wiped out by a tornado in July 2012, to asses the impact of forest management. To date, there have been more than 5 full years of continuous carbon and energy fluxes measurement, allowing insight into forest regeneration patterns due to manual reforestation, as well as differences in CO<sub>2</sub> losses connected to chosen treatments. The two sites (Tlen I and Tlen II) differ mostly in terms of soil preparation – at Tlen I site soil was ploughed before replanting and at Tlen II soil cover remained almost intact. Additionally, at the second location, only trunks and main branches were harvested, while all uprooted stumps were left to decompose. Both meteorological and soil conditions have been investigated, with most of them not being significantly different, which allowed drawing the conclusion that observed differences in GHGs balance <span>are most likely related to chosen forest management practices</span>. Thorough analysis of quality checked EC data revealed that in 5-year perspective the application of traditional method (Tlen I site), mainly due to soil ploughing, resulted in much less total CO<sub>2</sub> loss to the atmosphere, reaching C-neutrality point in only 6 years after the damage as well as better seedling growth in general in comparison to the technique, where the soil cover was not disrupted. Moreover, it seems that furrows created at the conventionally managed forest site (“double” organic layer) serve as crucial water reservoirs during water shortage periods, preventing from the pine plantation damage caused by prolonged droughts.</p><p>This work advances our understanding of how different forest management practices can help to sustain the least CO<sub>2</sub> losses on the example of wind-disturbed forests. Although, it has to be remembered that long-term studies are needed to point the best option from the perspective of climate change mitigation. </p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".