Plant growth and nutrient uptake in treatment wetlands for water with low pollutant concentration
Bibliographic record
Abstract
Abstract The objective of this study was to determine how macrophytes commonly used in treatment wetlands (TWs) respond to water with low pollutant concentration. We measured pollutant removal efficiency and compared growth and nutrient uptake of five macrophytes in demonstration scale units (volume >40 m3) irrigated by water with pollutant concentrations representative of average urban stormwater quality. All species showed a strong productivity gradient along the beds, starting with high biomass – high density near the inlet, then decreasing progressively with distance. Cyperus was by far the most productive species. Phragmites and Thalia had higher biomass in the first few metres of the beds than Typha and Arundo. In terms of pollutant removal, decreasing plant growth may be interpreted as indicative of high efficiency when caused by nutrient depletion. Differences in aboveground biomass between species did not translate into measurable differences in removal efficiency at the outlet. Although Phragmites australis is the species most commonly used in TWs, under the low nutrient load, Cyperus had twice its biomass, and higher N and P uptake. These results highlight the importance of considering wastewater characteristics when selecting macrophyte species for TWs.
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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".