Sediment-water nutrient exchange across a diked Lake Erie coastal wetland, Ohio, USA
Bibliographic record
Abstract
Coastal wetlands have the potential to mitigate nutrient pollution to the Great Lakes; however, a deeper understanding of how nutrients cycle between wetland sediment and surface water is still needed to calculate more accurate nutrient budgets. Using in situ and ex situ methods, we measured nutrient exchange at the sediment–water interface across a diked Lake Erie wetland (Turtle Creek Bay, Magee Marsh Wildlife Area, Ohio, USA). The wetland was recently reconnected to the neighboring stream with the goal of nutrient removal as part of the H2Ohio Initiative; however, historical management was aimed at creating waterfowl habitat. Vegetation now grows in distinct monotypic patches throughout the wetland. We characterized the site into five patches dominated by different vegetation groups: Typha spp., hardwoods, emergent, submerged, and floating vegetation. Sediments underlying emergent and submerged vegetation typically had greater rates of dissolved reactive phosphorus retention than those underlying hardwoods, but only when measured ex situ. Sediments in most patches released ammonium when measured ex situ but retained ammonium when measured in situ (using stacked resin bags), suggesting that ex situ intact sediment core incubations may overestimate the magnitude of nutrient exchange rates. Regardless of vegetation patch, nutrient exchange was negatively related to surface water nutrient concentrations and positively related to sediment nutrient concentrations, suggesting that diffusion is an important driver of nutrient retention and release. Our results focus on understanding nutrient exchange at the patch scale which can inform more accurate models for estimating whole system nutrient removal potential in Great Lakes coastal wetlands.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".