The Role of Boreal Seagrass Meadows in the Coastal Filter
Bibliographic record
Abstract
Abstract By removing nutrients from the water, coastal ecosystems serve as a filter between land and the open sea. Seagrasses contribute to the coastal filter by trapping and absorbing nutrients. Understanding the processes and environmental conditions underpinning the variability in nutrient retention among and within seagrass meadows is important to evaluate their role in the coastal filter across geographic regions, especially in less studied regions. This study evaluates the role of eelgrass (Zostera marina) meadows in the coastal filter in boreal Newfoundland, Canada, and identifies environmental traits driving variability in nutrient fluxes. We measured carbon (Corg) and nitrogen (N) proportions and stable isotopic composition in the surface sediment (top 5 cm) of three eelgrass meadows. Sediment cores were collected from different locations (i.e., inside, edge, outside) relative to each meadow. Sediment %N (0.22%), %Corg (2.82%), Corg stock (11.1 Mg Corg ha−1), and N stock (0.91 Mg N ha−1) were elevated in our study sites; however, nutrient content was not consistently higher inside the meadow than at the edge or outside. Variability in nutrient retention was best explained by a negative relationship with sediment bulk density. Additionally, differences in carbon isotopic (δ13Corg) enrichment between eelgrass tissue (−11.6‰) and sediment (−22.1‰) within sites indicated that sediment nutrients were predominantly derived from allochthonous marine sources, where variability was best explained by salinity. This study improves the understanding of the role of eelgrass to nutrient cycles in boreal coastal systems and the potential of eelgrass as a blue carbon ecosystem.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".