Biomanipulation: a useful tool for freshwater wetland mitigation?
Bibliographic record
Abstract
Summary 1. Natural wetlands have traditionally been considered as efficient ‘ecological engineers’ for waste water treatment. However, the structure and function of many natural wetlands have been severely altered by the chronic exposure to pollutants, especially nutrients. 2. Despite the similarity of symptoms of eutrophied shallow lakes and wetlands, restoration strategies differ distinctly between these rather similar aquatic systems. Many of the tools applied in shallow lake restoration programs, for example biomanipulation, have received little attention in wetland management and restoration. 3. Although a strong conceptual basis for food web management exists, biotic interactions as influences on wetland communities have been largely neglected by wetland scientists and managers. 4. In this paper we show that biomanipulation may have a strong potential for wetland eutrophication abatement. This potential will be demonstrated by reviewing studies carried out in different wetland types in contrasting climatic regions. 5. We propose four different scenarios for when, where and why biomanipulation may be used to rehabilitate freshwater wetlands. These scenarios reflect different settings of hydrological variability, eutrophication sources and gradients of wind exposure and water colour.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".