Multi-decadal impacts of effluent loading on phosphorus sorption capacity in a restored wetland
Bibliographic record
Abstract
Natural wetlands are widely used and cost-effective systems for the passive remediation of phosphorus (P)-rich surface waters from various effluent sources. Yet the long-term biogeochemical impacts of effluent loading on wetland P retention capacity are unclear. Here, we had a unique opportunity to document the spatio-temporal evolution of sediment P sorption over a ∼25-year period of constant municipal and industrial effluent loading, as part of a wetland restoration and wastewater treatment strategy in one of the largest restored wetlands in Canada. Sediment P sorption experiments across Frank Lake's three basins revealed a wide spatial variation in sorption capacity, closely linked to sediment geochemistry gradients (Ca, Fe, and Mn). Relative to a similar study ∼25 years prior, P sorption capacity has become exhausted near the effluent inlet, but remarkably, remains elevated throughout the rest of the wetland. Compared to other prairie wetlands and global aquatic ecosystems, Frank Lake has a greater capacity overall to retain P through sediment sorption. Given the paucity of long-term (multi-decade) data on wetland response to effluent loading, we provide key insights into the dynamics of wetland P cycling in human-dominated watersheds. • Geochemical properties predicted P sorption in a restored wetland receiving effluent. • The wetland remains a P sink due to high SRP content and extensive sediment area. • Restoration with effluent uniquely shapes sediment geochemistry in prairie wetlands. • Prairie wetlands have high P retention capacity relative to global aquatic habitats.
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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".