A circular economy approach for geotextile reuse following lake water filtration
Bibliographic record
Abstract
Eutrophication in lake systems is intensifying. To reduce this possible scenario, a method for suspended solids and associated nutrient removal by a novel on-site remediation has been investigated, using nonwoven geotextiles as filter media. These procedures generate clogged geotextile layers with captured suspended solids on them. To become more sustainable, circular economy principles were employed, more precisely reuse. Thus, this investigation aims to assess the potential reuse strategies by washing clogged layers and determining their possible reuse. The washing method was pressurized water (i.e., using a gardening pump sprayer). Preliminary results have shown the efficiency of the washing method in removing visible geotextile (non-woven) clogging, with permeate flow rates reaching values close to the initial process values. The geotextile apparent opening size increased by an order of 20%. Also, no geotextile fibre disruption was observed by scanning electron microscope (SEM) imagery, indicating its possible reuse. The dilute liquid waste preliminary findings showed high concentrations of some metals such as manganese, (112.72 μg/L) and zinc (88.12 μg/L) in addition to phosphorus (120.18 μg/L) which requires additional studies. The washed geotextile leaching test did not indicate any contaminants in the permeate which would enable geotextile layer reuse for lake water filtration.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".