Nitrogen and Phosphorus Phytoextraction by Cattail ( <i>Typha</i> spp.) during Wetland‐based Phytoremediation of an End‐of‐Life Municipal Lagoon
Bibliographic record
Abstract
Spreading biosolids on farmland can be an effective and beneficial option for managing end-of-life municipal lagoons. Where the spreading of biosolids on farmland is restricted or unavailable, in situ phytoremediation could be a sustainable alternative. This study examined nitrogen (N) and phosphorus (P) phytoextraction by cattail ( spp.) from biosolids in a wetland constructed within a lagoon cell previously used for primary treatment of municipal wastewater. The effect of harvesting season as well as harvest frequency on N and P removal were evaluated. Forty-eight 4-m plots within the constructed wetland were used to determine the effect of cattail harvest frequency on plant N and P phytoextraction. Harvesting twice per season resulted in a 50 to 60% decrease in phytoextraction of N and P relative to a single harvest per season, which produced biomass yields of 0.58 to 0.6 kg m per year and accumulated 36.7 g N m and 5.6 g P m over the 4-yr period. Compared with August, harvesting cattails in November or April reduced N and P phytoextraction by 63 to 85%. These results demonstrate that phytoextraction of nutrients is more effective with a single harvest compared with two harvests per season. Additionally, we found that while harvesting in November and April is appealing logistically (since the wetland is frozen and provides easier access to harvest equipment), nutrient removal rates are significantly reduced.
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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".