Disturbance Effects on Soil Carbon and Greenhouse Gas Emissions in Forest Ecosystems
Bibliographic record
Abstract
Forests cover around 30% of the global land area and forest ecosystems can store over 70% of total soil organic carbon (SOC) of all terrestrial ecosystems, but SOC stocks and greenhouse gas (GHG) emissions may be affected by both natural and anthropogenic disturbances. Even though the changes in forest soil C pool can have a significant effect on climate change, there are some contradictory results regarding the role of forest disturbance on SOC sequestration, GHG emissions, and the mitigation of global changes. Therefore, there is a need to better understand the impact of different disturbance regimes on forest soil C storage and GHG emissions. A Special Issue was therefore organized for discussing the responses of soil C storage and GHG emissions to various types of disturbances in forest ecosystems and a total of 15 studies were accepted for this special issue to assess these responses. This Special Issue includes the effects of storms and beetle outbreaks, Karstification, rock desertification, warming, nitrogen addition, land-use change, field tillage, and biochar application on soil C dynamics and/or GHG emissions.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".