Influence of nutrients enrichment on ecosystem functioning in a subpolar seagrass meadow
Bibliographic record
Abstract
Seagrass meadows are among the most productive ecosystems in the world: they store a large amount of carbon and host highly diverse macrobenthic communities. They also play a key role in biogeochemistry at the sediment-water interface. The light requirements of seagrasses limit their development to shallow coastal areas where they are facing various natural and anthropogenic disturbances, which has induced a global loss of these ecosystems over the last decades. Nutrient enrichment of coastal waters, resulting from anthropogenic activities is one of the leading causes of this decline. Subpolar seagrass meadows present a strong seasonal dynamic, with a long winter when seagrasses rely on carbon reserves that they build up during the short growing season (limited to two to three months during summer time). Hence, it has been hypothesized that the effects of nutrient enrichment on seagrass ecosystem functioning depend on seasonal dynamics. In this study, we performed a series of mesocosm experiments over a month period to investigate the effects of the timing, duration and intensity of disturbance on macrofauna bioturbation, oxygen and nutrients porewater concentration profiles and benthic fluxes using three levels (including control) of realistic nutrient enrichments at the beginning (June) and at the end (August) of the growing season. In May, effects of intermediate level of nutrient enrichment were only visible on total oxygen uptake by the sediment at day 30 of disturbance while it affected oxygen and nutrients benthic fluxes at day 15 in August. The highest level of nutrient enrichment affected oxygen and nutrients benthic fluxes in May and August. Overall, our results highlight the importance of considering the time (period and duration) in the assessment of the functional consequences of disturbances.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".