Potential of Macrophytes for Wastewater Remediation with Constructed Floating Wetlands in Cold Climates
Bibliographic record
Abstract
Nature-based, low technology wastewater treatment systems can benefit small and remote communities. Adding a constructed floating wetland (CFW) to waste stabilization ponds can enhance treatment efficacy at low cost, depending on appropriate macrophytes. In cold climates, harsh growing conditions may limit CFW success, requiring research under-ambient field conditions. Seven native macrophytes were assessed for the growth, biomass production, and root and shoot uptake of potential contaminants of concern from municipal wastewater in a facultative stabilization pond in Alberta, Canada. All macrophytes established. Scirpus microcarpus had high nitrogen and phosphorus in roots and shoots and phytoextraction potential. Metal and trace elements were highest in Glyceria grandis, Beckmannia syzigachne, and Scirpus microcarpus, mostly greater in roots than shoots, indicating phytostabilization. Tissue contaminant concentrations did not always indicate high contaminant accumulation in the CFW. Total uptake per unit area was greatest for Glyceria grandis, although chromium and molybdenum were greatest in Beckmannia syzigachne and Carex aquatilis, respectively. Beckmannia syzigachne and Scirpus microcarpus have potential for phytoremediation if biomass per unit area is increased. Species variability is high for contaminant accumulation and biomass; in unpredictable climates and wastewaters with suites of contaminants, different macrophytes for wetland water treatment systems are recommended.
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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 | 0.001 | 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".