Removal of phosphorus and other components from Eutrophic Lake water
Bibliographic record
Abstract
Increasing phosphorus (P) content and decreasing water quality of shallow Lake Caron, 75 km north of Montreal, Canada has led to the implementation of many management strategies to restore the lake. Despite the perceptible reduction in external sources, except drainage from nearby forest areas, the P level in lake water still remain high and constitute a major challenge to the lake restorers. As part of the restoration plan, a laboratory study was performed to assess the effectiveness of non-woven geotextiles in reducing the nutrients (P and nitrogen, N) concentrations and improving the lake water quality. Two geotextile filters (TE-GTN350 and TE-GTX400) with different apparent opening sizes and materials were tested in three different combinations: (i) TE-GTN350/TE-GTN350, (ii) TE-GTX400/TEGTN350 and (iii) TE-GTX400/TE-GTX400. Apart from geotextile filters, clean sediments were incorporated onto the filters, and these sediments may act as adsorbent materials for nutrients and enhance the treatment efficiency. Due to filtration, the total P (TP) content was reduced from 40 mug L-1 to 10 mug L-1, which is the safest level of P for the protection of aquatic life in Quebec surface waters. Overall TP removal efficiency by filters was between 62.5 and 75% and a slightly higher efficiency was observed with TE-GTN350/TE-GTN350 combination. Apart from TP removal, these filters were effective in reducing the turbidity by 77-85% and total N (TN) by 37-52%. The water quality improved in terms of nutrients and turbidity removal rendering an effective treatment system with potential for on-site testing as the next step.
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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) | 0.001 | 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".