Water darkening modulates the efficiency of coastal eutrophication mitigation in a mesocosm experiment
Bibliographic record
Abstract
To mitigate coastal eutrophication, a worldwide concern to sustain ecosystem services, actions often focus on nutrient-load reductions but sometimes also aim to limit the abundance of forage fish. Adding to the eutrophication problem, changes in climate and land use lead to increased inputs of coloured dissolved organic matter (darkening). We conducted a mesocosm experiment to study the effects of water darkening on the outcome of eutrophication mitigation actions acting either bottom-up or via top-down control. We initiated eutrophication under varying water colour conditions. Then, we performed mitigation actions by stopping nutrient additions, removing forage fish, or both. Stopping nutrient additions was the most effective action in reducing pelagic algal biomass, irrespective of water colour. In contrast, the effect of fish removal varied with water colour, only having an effect in darker waters. We link these varying responses to shifts in nutrient availability and zooplankton community biomass and composition. Our experiment demonstrates that future eutrophication management may need to consider multiple concurrent pressures, including coastal darkening, as they can generate indirect effects caused by trophic interactions.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".