A Field Bioassay of Nitrogen and Phosphorus Phytoextraction from Biosolids in a Seasonally Frozen End‐of‐Life Municipal Lagoon Vegetated with Cattail
Bibliographic record
Abstract
Managing biosolids from end‐of‐life municipal lagoons is a major challenge for many small communities where landfilling or spreading of biosolids on farmland is restricted. Contaminant removal via phytoextraction may be a viable remediation option for end‐of‐life lagoons in such communities. This study examined the effect of harvest frequency (once or twice per season) on cattail (Typha latifoliaL.) biomass yield and N and P removal under a terrestrial phytoremediation system designed to treat the dewatered secondary cell of a municipal lagoon in Manitoba, Canada. Cattail was harvested once or twice per season from eight vegetation transects, each divided into two plots (2.5 × 2.5 m) to accommodate the two harvest frequencies. Biomass yields were greater for the single harvest (5.7 t ha−1yr−1) than for two harvests per season (4.8 t ha−1yr−1). This was mirrored by N phytoextraction, which was also greater for the single harvest (71 kg ha−1yr−1) than the two‐harvest frequency (58 kg ha−1yr−1). Phosphorus phytoextraction varied with year of harvest and ranged from 8 to 14 kg ha−1yr−1. Cumulative N and P phytoextraction amounts during the 5 yr were 330 kg N ha−1and 57 kg P ha−1. A greater fraction of N (51–91 kg ha−1yr−1) and P (23–40 kg ha−1yr−1) was sequestered in the belowground biomass (11–17 t ha−1yr−1) and therefore was not removed by harvesting. These results show that phytoremediation using cattail is a viable option for managing N and P in end‐life lagoons. Core Ideas Seasonally frozen end‐of‐life municipal lagoon was amenable to terrestrial phytoremediation. We effectively phytoextracted N and P from municipal biosolids. Cattail biomass yields were greater with one than two annual harvests. Cumulative N and P phytoextraction was greater with one than two annual harvests. Greater fractions of N and P were sequestered in the belowground biomass.
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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) | 0.001 | 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".