Towards a Fundamental Model to Predict the Settlements in Bioreactor Landfills
Bibliographic record
Abstract
Settlement prediction is one of the main concerns in design and maintenance of a bioreactor landfill. Accurate prediction is essential for design of piping systems used for the delivery of re-circulated leachate and collection of gases. Though the major component of settlement is due to the decomposition of municipal solid waste over several years, considerable amount of settlement takes place during the initial construction stage too, which is usually unnoticeable as it happens during construction. A comprehensive model for settlement analysis of a bioreactor landfill should be able to demonstrate not only the settlements due to biodegradation but also the settlements that occur due to mechanical compression during initial construction stage. Often an overall compressibility index is defined similar to that of clays, to explain compressibility of waste, but not much attention has been paid to the settlement behavior during the initial stage or during construction. This manuscript describes a procedure to compute settlements due to mechanical reasons in a bioreactor landfill by separating that from the biodegradation. Then a new conceptual framework was proposed to numerically predict the settlements using time dependent waste properties and landfill geometry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".