A model to estimate the methane generation rate constant in sanitary landfills using fuzzy synthetic evaluation
Bibliographic record
Abstract
This paper presents a model using fuzzy synthetic evaluation to estimate the methane generation rate constant, k, for landfills. Four major parameters, precipitation, temperature, waste composition and landfill depth were used as inputs to the model. Whereas, these parameters are known to impact the methane generation, mathematical relationships between them and the methane generation rate constant required to estimate methane generation in landfills, are not known. In addition, the spatial variations of k within a landfill combined with the necessity of site-specific information to estimate its value, makes k one of the most elusive parameters in the accurate prediction of methane generation within a landfill. In this paper, a fuzzy technique was used to develop a model to predict the methane generation rate constant. The model was calibrated and verified using k values from 42 locations. Data from 10 sites were used to calibrate the model and the rest were used to verify it. The model predictions are reasonably accurate. A sensitivity analysis was also conducted to investigate the effect of uncertainty in the input parameters on the generation rate constant.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".