Particle Size and Clogging of Granular Media Permeated with Leachate
Bibliographic record
Abstract
The effect of particle size (4-, 6-, and 15-mm nominal sizes) on the rate of clogging of columns of porous media permeated with municipal solid-waste leachate is examined. Clogging is shown to be more localized over a small volume of the porous media near the influent end of the column for smaller particles than for larger particles, where clogging was more uniformly distributed along the column. This is attributed to the greater surface area per unit volume of smaller particles allowing greater biofilm growth per unit volume. This increased the reduction in chemical oxygen demand (COD) and caused greater deposition of inorganic clog material per unit length of column than for larger particles. The distribution of methanogenic bacteria was found to closely correspond to the zones of most severe clogging. The bulk density of clog material is shown to be between 1.6 and 1.8 Mg/m3. The chemical composition of the clog material is essentially independent of particle size, with calcium representing 26% of the dry mass of the clog material and CaCO3 being the main component of the clog. An examination of the yield of CaCO3 relative to COD indicates that the carbon in the CaCO3 represents <4% of the organic carbon represented by the drop in COD. Finally, the data from the column test is used to predict the expected time to clog for an actual landfill and were found to give results consistent with what was observed in the field.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".