Effect of filter–separators on the clogging of leachate collection systems
Bibliographic record
Abstract
This paper reports the results obtained after 6 years operation of nine mesocosm experiments that simulate the 50 cm of the drainage layer closest to the leachate collection pipe in a landfill. Five different design configurations were examined involving a 300 mm thick layer of coarse (38 mm) gravel. The designs differed in terms of the presence, nature, and location of a filter–separator layer either at the waste–gravel interface or partway through the gravel. A nonwoven geotextile filter–separator (GTF/S) is shown to reduce clogging of the gravel relative to the no filter–separator or woven GTF/S designs. Some clogging of the geotextiles is reported, with reductions in geotextile hydraulic conductivity of 23% for the woven GTF/S, 74%–89% for the nonwoven GTF/S, and 75%–94% for the nonwoven geotextile partway through the gravel. The clogged nonwoven geotextile filter–separator maintained a higher hydraulic conductivity than the extracted woven geotextile. Of the designs with a filter–separator between the waste and gravel, the granular filter–separator most effectively reduced clogging of the gravel but at the expense of leachate mounding above the sand once the sand layer clogs. The design with a nonwoven geotextile partway through the gravel (GTMF) provides better protection of the underlying gravel from clogging than other designs involving a geotextile.Key words: landfill, waste, leachate, clogging, biofilm, geotextile.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".