Assessment of Gaseous Emissions from Cattle Abattoir Wastes in Cameroon
Bibliographic record
Abstract
Abattoirs are potentially a significant source of greenhouse gas (GHG) methane (CH4) and nitrous oxide (N2O) emissions. Measurements were conducted in a beef cattle abattoir located in Bamenda, Cameroon, to characterise waste production and quantify GHG emissions. A male and female cattle were randomly selected on each day for waste measurement over a period of two weeks. Waste from each cattle was quantified by collecting all the intestinal/stomach contents after slaughtering and determining the mass of dry matter (DM) and volatile solids (VS). Emissions from the outdoor solid waste storage heap was measured using flux chambers. The average cattle weight was 420 kg and the average intestinal/stomach waste was 37 ± 6 kg cattle−1, half of which was dumped outdoor in a heap, while the rest was discarded with wastewater into a stream. The DM produced was 4.19 ± 0.85 kg cattle−1, representing 11% of the wastes, and the VS produced was 3.42 ± 0.82 kg cattle−1. The average ratio of waste DM to cattle weight was 1.0%, while the ratio of waste VS to cattle weight was 0.8%. Modelled CH4 emissions from the total waste was estimated at 37.84 ± 8 g CH4 cattle−1 with a range of 27.57–56.03 g CH4 cattle−1. Measured GHG emission from the outdoor heap was 5.89 ± 4.78 mg CH4 m−2 min−1, 0.137 ± 0.151 mg N2O m−2 min−1, and 95 ± 83 mg CO2 m−2 min−1. The total GHG (CH4 + N2O) emission rate was 229 mg CO2e m−2 min−1, indicating that CH4 contributes 82% of the total GHG. Improved waste management strategies, such as anaerobic digestion for biogas production or using covers over waste heaps, would help abattoirs mitigate GHG emissions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".