Application of the BioClog model for landfill leachate clogging of gravel-packed columns
Bibliographic record
Abstract
A numerical, multiple-species, reactive chemical transport model (BioClog) developed to predict clogging in landfill leachate collection systems is used to interpret results from experiments conducted with gravel-packed columns permeated with landfill leachate. The model predicts changes to the microbial community and leachate chemistry, including the concentrations of volatile fatty acids, suspended biomass, dissolved calcium, and suspended inorganic solids. The calculated quantity and composition of the clog matter (biomass and mineral), along with the associated decrease in porosity, are compared to the measured values. The modelled clogging is in reasonable agreement with that observed in the gravel column experiments. By identifying and quantitatively linking many microbiological, chemical, and transport mechanisms, the model helps elucidate the phenomena controlling the rate and extent of clogging.Key words: clogging, landfills, leachate collection systems, biofilms, mineral precipitation.
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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 | 0.001 | 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".