Bibliographic record
Abstract
This study was carried out to develop a model capable of predicting the generation and potential emission of methane gas into the environment. Ryerson Landfill Gas Model (RLFGM) was developed using the IPCC tier two model (revised guidelines), the most recent national assessment model (Brown et. al. 1999) from UK and the model developed from DEFRA, UK. This report outlines the findings of the study and its recommendations. The study was performed on detailed characterization of solid waste as RDO, MDO and SDO and by dividing these waste components into nine waste fractions. The waste fractions are described by the percentage of moisture content of the fraction, the proportion of cellulose and hemi-cellulose, and the percentage of the degradability of the cellulose and hemi-cellulose fraction. Methane generating potential (L₀) was calculated from DOC and DOC(subscript f) value. The three methane generation rate constants (k) were used. Methane oxidation, methane correction factor, percentage of methane gas recovery, percentage of methane content in landfill gas were assumed based on comprehensive literature review. After the verification of the output of the RLFGM model with the LandGEM model it was found that the model is producing similar type of output graph as the LandGEM model but the rate of emissions of methane gas into the environment is lower in RLFGM model. It has been concluded that RLFGM model gives the realistic output with respect to individual landfill sites, taking into account of specific waste streams and deposition rates.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".