Predicting Bioreactor Landfill Air Space by Estimating Geotechnical Properties of Waste
Bibliographic record
Abstract
Intensive research has focused on the settlement of the typical Municipal Solid Waste (MSW) in bioreactor landfills, but relatively little attention has been given to the settlement of individual refuse components. The objective of this paper is to estimate and compare the compressibility parameters of different waste fractions, such as: textile, paper, and mixed waste through measuring the change in the physical properties, and settlement characteristics of waste in six lab-scale bioreactor landfills operated under anaerobic conditions. Primary compression index (Cc), and coefficient of volume compressibility (mv) were estimated for all three waste materials using time-settlement data. The primary compression index (Cc) increased from 0.31 for textile waste to 0.45 for paper waste, and 0.63 for mixed waste. It can be noted that Cc increased with increasing the waste organic content. The value of the coefficient of volume compression (mv) suggests that the biodegradation increased the values of mv of all types of waste samples. Textile waste incorporated the lowest value of mv compared to all other solid waste fractions. This may be attributed to the fact that the textile is slowly biodegradable compared to paper and food wastes as stated earlier. Textile waste cells had the least value for all compressibility parameters. Proper estimation of the waste compressibility parameters would allow engineers and landfill designers formulate mathematical models to better estimate available air space saving and expected time dependent deformation patterns at field scale bioreactor landfill cells, which subsequently increases life time of bioreactor landfills.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".