An In-Situ Geotextile Filtration Method for Suspended Solids Attenuation and Algae Suppression in a Canadian Eutrophic Lake
Bibliographic record
Abstract
Climate change and human actions will exacerbate eutrophication cases in inland waters. By external or internal inputs, there will be an increase in nutrient concentrations in those systems worldwide. Those nutrients will bring faster trophic changes to inland waters and possible health and recreational advisories. A novel approach using a floating filtration system, a silt curtain, and geotextiles (woven and non-woven) is under investigation. This method has been applied as an in-situ pilot experiment deployed at Lake Caron, a shallow eutrophic lake in Quebec, for two summers. Turbidity, total suspended solids (TSS), total phosphorus (TP), blue-green-algae-phycocyanin (BGA-PC) and chlorophyll-a showed statistically significant average removal efficiencies of 53%, 22%, 49%, 57% and 56%, respectively, in the first year and 17%, 36%, 18%, 34% and 32% in the second. Statistical correlations were found with TSS, turbidity and variables that could represent particles (TP, turbidity, chlorophyll-a). Employing this in situ management method could be a promising remediation for not only shallow lakes (average depth < 2 m) but also for ponds, rivers, coastal regions, bays and other water types, to enable cleaner water for future generations.
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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 | 0.001 | 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".