Tropical artificial rural and urban ponds are net sources of carbon dioxide and methane in Rwanda, East Africa
Bibliographic record
Abstract
Artificial ponds have been overlooked as sources of greenhouse gases (GHGs) despite their potential to be significant emission sources. We studied the concentration of dissolved organic carbon, [DOC], and the concentration and fluxes of CO 2 and CH 4 in five rural fishponds and five ornamental urban ponds with areas of 981 to 3,676 m 2 in the capital of Rwanda, Kigali. The mean concentration of DOC in rural ponds (60.12 ± 4.70 mg L –1 ) was lower than that in urban ponds (69.61 ± 5.97 mg L –1 ). The dissolved CO 2 concentration in rural ponds (24.20 ± 2.40 μmol L –1 ) was also lower than that in urban ponds (30.40 ± 8.61 μmol L –1 ). However, the concentration of CH 4 in rural ponds (3.31 ± 1.16 μmol L –1 ) was ∼6 times higher than that in urban ponds (0.59 ± 0.11 μmol L –1 ). Areal CO 2 fluxes in rural ponds (8.07 ± 1.57 mmol m –2 d –1 ) were slightly higher than those in urban ponds (7.86 ± 3.33 mmol m –2 d –1 ). Areal CH 4 fluxes in rural ponds (1.77 ± 0.62 mmol m –2 d –1 ), were 7 times higher than in urban ponds (0.25 ± 0.05 mmol m –2 d –1 ). The mean C flux in CO 2 equivalents (CO 2 -eq) from all ponds was 275.53 g CO 2 -eq m –2 yr –1 , of which 53% was attributed to CH 4 . These findings highlight the need to include artificial ponds in national and global greenhouse gas inventories to their overall carbon footprint.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".