Editorial: Wetland ecology and biogeochemistry under natural and human disturbance- volume II
Bibliographic record
Abstract
This special issue consists of six original papers and one review paper, which attempted to help address the abovementioned issues. Recent findings on the effects of climate change and human disturbances on wetland functions and biochemical processes have been reported.Wetlands are essential carbon sinks due to their higher productivity than respiration (Noumonvi et al.). Ecosystem respiration (ER) is composed of autotrophic respiration (AR) that consists of respiration by plant parts, and heterotrophic respiration (HR) that consists of respiration by microbial bacteria. With climate warming and human drainage, ER could increase (Zhou et al.), and the contributions of HR and AR to the ER can be altered (Rankin et al.). Not only respiration but also methane (CH4) emission is affected by the climate and human activities. It has been reported that CH4 emission in wetlands would increase 50-80% by the year 2,100 under climate change (Koffi et al., 2020) and significantly increase under human disturbances (Benavides et al.).Besides carbon cycling, nitrogen cycling in wetland ecosystems is also affected by climate change and human disturbances. For instance, climate warming could mitigate the positive effect of nitrogen deposition on nitrous oxide emissions (Gong & Wu, 2021;Gong et al., 2019). Urease and nitrate reductase activities have been reported to increase with climate change and human activities (Chang et al.).The papers brought together a wide range of aspects related to the impacts of climate change and human disturbances on wetland ecosystems, which helps us better understand the carbon and nitrogen cycling of wetlands. In addition, the dominant drivers and biogeochemical dynamics for disturbed wetlands have been well illustrated, which benefits the development of wetland management and restoration strategies.
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 | 0.001 | 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 | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".