Preliminary Evaluation of a Sediment Resuspension Technique for Reduction of Phosphorus in Lake Water
Bibliographic record
Abstract
Lake Caron is a shallow and hypereutrophic lake situated 75 km from Montreal, Quebec, Canada. This study is aimed to evaluate the resus-pension of sediments in Lake Caron, followed by filtration of the resuspended particles. The intention is to improve water quality by removing excessive phosphorus. The effectiveness of the resuspension technique could be directly evaluated by measuring phosphorus content in water before and after the resuspension. It was shown that resuspension successfully improved the water quality by reducing phosphorus content in water by 96.7 % compared to the initial concentration. To understand the retention of phosphorus in the surface sediments, selective sequential extraction was also performed. Core sediment samples were also collected along with the surface sediment. The phosphorus content in surface sediments was 131 mg/g, whereas the sediment from the bottommost layer resulted in 28.7 mg/g phosphorus content, a 64.5 % difference. The phosphorus distribution was also calculated on the basis of different particle sizes. Particles which were smaller than 45 lm showed 37 % higher phosphorus content on average than the particles larger than 75 lm. Therefore, these results are a preliminary indication of the potential for resuspension as a method for managing the phosphorus content in sediments.
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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 | 0.001 | 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".