Bibliographic record
Abstract
Abstract Wetlands are ubiquitous over the globe, comprise a vast array of ecosystem types and are of great ecological and social importance. Their functioning is intimately tied to the atmosphere by the energy and mass exchanges that take place across the wetland–atmosphere boundary. This article examines recent research into these exchanges, with an emphasis on the water vapour exchange. Although broad classes of wetland type, such as fen, bog and marsh, can be defined using ecological or hydrologic metrics, distinct difference in energy exchanges between the classes cannot be found. This arises because there are many factors that control the energy exchanges and interplay of these factors is unique to every wetland ecosystem. Wetlands are more similar in their radiation balances than in the partitioning of this energy into conductive and turbulent heat fluxes. This is especially true of evapotranspiration (ET) rates, which vary considerably among and within wetland classes. A global survey of wetland ET studies shows that location has little to do with ET rates and that variation in rates is largely determined by local climate and wetland characteristics. Recent modelling studies suggest that although wetlands occupy a small portion of the global land surface, their water and energy exchanges may be important in regional and global climates. Although the number of studies of wetland–atmosphere interactions has increased in recent years more research is needed. Five key areas of study are identified: (i) the importance of moss covers, (ii) lack of study in tropical systems, (iii) inclusion of wetlands in global climate models, (iv) importance of microforms in wetlands and their scaling to the whole ecosystem, and (v) the paucity of annual ET measurements.
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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".