Methane Emissions from Landfills Sites and Their Contribution to Global Climate Change in the Greater Lomé Area of Togo (West Africa)
Bibliographic record
Abstract
This study was carried out in the city of Lomé in Togo. The study looked at the contribution of illegal waste landfills to climate change. The focus was on the quantities of methane released by uncontrolled landfills. In order to achieve the objectives, set by this study, the quantity of methane was recorded at twenty (20) landfills in thirteen (13) localities using microsensors over a period of thirty-two (32) days. The measurements were taken at the landfills with the measuring device stationed in the middle of the landfill at a height of 25 cm above the waste. The data collected was processed and a probability diagram was drawn up, making it possible to assess whether or not a set of data follows a given distribution such as the normal or Weibull distribution. Similarly, the contribution of each of the landfills to climate change was determined. During the measurement period, it was found that the TOGBLEKOPE 2 (6.338 g/m3 ± 4.881) with a contribution of 133.09; AMOUTIEVE (5.565 g/m3 ± 2.889) with a contribution of 116.86; ADETIKOPE GUERINKA (5.56 g/m3 ± 2.123) with a contribution of 116.76; GBOSSIME (5.323 g/m3 ± 4.442) with a contribution of 111.78; HOUNBI (4.702 g/m3 ± 3.59) with a contribution of 98.742; ADETIKOPE KPETAVE (4.363 g/m3 ± 2.841) with a contribution of 91.62 and NYEKONAKPOE 2 (4.017 g/m3 ± 3.067) with a contribution of 84.357; release more methane into the atmosphere. This shows the contribution of landfill sites in the fight against climate change.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".