Effect of Sand Capping on Phosphorus Release from Phosphorus-Enriched Coastal Wetland Sediments: A Laboratory Study
Bibliographic record
Abstract
Restoration of coastal wetland habitat in the Laurentian Great Lakes often includes addressing excess soil nutrient levels because of prior land use. A wetland restoration project is underway in former celery fields located at the mouth of Mona Lake (MI), a drowned rivermouth system that drains directly into Lake Michigan. One approach proposed to address high phosphorus (P) levels in the sediment is sand capping. This study examined the effectiveness of two sand sources (within-pond and quarry) and two sand cap thicknesses (15 and 30 cm) in inhibiting P release from the native sediment using sediment cores incubated in the laboratory. Our results indicated that there was not a strong difference in sand type or capping depth on P release. Both sand sources resulted in an approximate total phosphorus (TP) reduction of 60% - 70% over time, although this still left TP concentrations well above desirable levels. The quarry sand did better than pond sand in reducing TP, whereas pond sand did better with respect to soluble reactive phosphorus (SRP). Given the relatively small differences in P reduction performance, pond sand is recommended because it is already present on site and therefore is less expensive and easier to move. In addition, pond sand had higher amounts of apatite than quarry sand, suggesting more of the attached P will remain in a stable form. Sand capping is a relatively inexpensive approach for reducing P levels in coastal wetlands, although it may need to be paired with other approaches to reduce nutrient concentrations to desired levels.
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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) | 0.001 | 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 | 0.001 |
| 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".