Bibliographic record
Abstract
Landfills accepting biodegradable organic waste produce significant amounts of methane richlandfill gas. Landfill gas, or LFG, constitutes approximately equal quantities of methane andcarbon dioxide. The global emissions of methane from landfills are estimated to be about 10%oftotal anthropogenic emissions. Methane, a greenhouse gas, has a global warming potential(GWP) 21 times that of carbon dioxide over a laO-year time horizon. Therefore, control ofmethane from anthropogenic sources could substantially mitigate global warming. Consideringthese factors, there is renewed interest in controlling methane emissions from landfills.Methane emissions from landfills can be controlled by extracting LFG for energy recovery,passive venting and flaring, or by modifying the landfill cover to enhance passive oxidation ofmethane to carbon dioxide. To design any of the LFG control techniques prior knowledge of theamount of gas available is necessary.LFG production is site specific and depends on factors such as climatic conditions, wastecharacteristics, spatial heterogeneity, age of landfill, geometry of the landfill etc. The currenttechniques used to estimate LFG production are: theoretical calculations, generation models,experimental studies, LFG pumping tests and direct flux measurement. This paper discusses theadvantages and disadvantages in using these methods to estimate LFG production. The paperconcludes with a newly developed method at the University of Calgary, Canada to estimate LFGproduction. The method incorporates a geostatistical technique and a l-D numerical modelwithin a Geographic Information System (GIS).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".