Mechanisms of clogging in granular drainage systems permeated with low organic strength leachate
Bibliographic record
Abstract
Leachate drains in municipal solid waste (MSW) landfills are susceptible to biological and (or) chemical clogging. This paper describes clogging of drainage aggregates permeated with leachates representative of those from landfills containing wastes with a low organic content — such as low-level radioactive waste repositories — which may occur as a result of microbiological activity causing formation of bacterial biofilms (microbiological clogging) and precipitation of low-solubility inorganic salts (chemical clogging). The balance between these depends on the leachate composition. Biological deposits appeared to reach a pseudo steady state, proportional to the nutrient loading, for the range of conditions investigated and as such can be considered to be self-limiting and the clog material reasonably permeable. Harder inorganic deposits of calcium carbonate occurred if the Ca2+ concentration in the leachate exceeded the local solubility limit under the prevailing conditions, i.e., partial pressures of CO2 between 3.7 and 6 kPa and pH of 6.7–6.8. CaCO3 clog was observed to bind the granular aggregates together and be effectively impermeable, and was, unlike a pure microbial clog, observed not to be self-limiting. Hard CaCO3 clog could be reduced by not co-disposing wastes that are high in calcium with wastes having a high organic content, and generally keeping the Ca2+ concentration in the leachate low.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".