Assessing the Native Plant Species for Phytoremediation of Freshwater Bodies in Southern Ontario, Canada
Bibliographic record
Abstract
Many Canadian freshwater ecosystems are polluted by agricultural runoff, impairing their function with increased nutrient levels. Here, we simulated the water filtration function of wetlands, which uses aquatic plant species to create a phytoremediation system that can address the contamination of freshwater ecosystems with excess nutrients. We collected the water samples from three of Ontario’s freshwater bodies: the Holland Marsh, a highly agricultural area; the Nottawasaga River, a river in a rural area and part of a greater Nottawasaga watershed and Lake Ontario, near industrial sites in the Niagara region. To filter nitrogen (N), phosphorus (P) and potassium (K) from the collected samples, we determined the effectiveness of five local wetland and agricultural plant species: duckweed (Lemnoideae), watercress (Nasturtium officinale), coontail (Ceratophyllum demersum), thyme (Thymus praecox) and parsley (Petroselinum crispum). During a five-month experiment, plants were grown in collected water samples to determine their ability to uptake N, P and K. Along with monitoring their effectiveness in lowering nutrient levels, we tracked the health and growth of each plant species. The results showed that duckweed was the most tolerant to high nutrient concentrations and the most effective at overall nutrient reduction. From the Holland Marsh sample with the highest nutrient concentrations among all collected samples, the duckweed reduced N, P, and K by 11%, 53%, and 21%, respectively, compared to the control sample (i.e., with no plant). This filtration system allows for ecosystem restoration and prevention of further damage and contamination from agricultural runoff and nutrient pollution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".