Modelling the clogging of coarse gravel and tire shreds in column tests
Bibliographic record
Abstract
A combination of data from laboratory tests and modelling is reported for both coarse gravel (19–38 mm) and two types of tire shred permeated with municipal solid waste leachate. It is suggested that the dispersivity of both the coarse gravel (initially about 4 mm) and tire shreds (initially about 45 mm) increases as the porosity of the drainage media is reduced because of clogging. The detachment of biofilm caused by growth and shear is examined and both are found to influence clogging. The average grain size estimated based on the measured surface area of the particles within a unit volume is shown to provide a good prediction of the rate of clogging for gravel and a conservative prediction for tire shreds. The size and density of suspended solids in leachate is found to significantly influence clogging rates. It is shown that Monod kinetic constants deduced for gravel at 27 °C give a good prediction of clogging for two different types of tire shred at the same temperature. Calibrated parameters used with the BioClog model are shown to give good fits to the porosity of both gravel and tire shred drainage material in laboratory column tests over time periods of up to 2 years.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".