The Importance of Ditches and Canals in Global Inland Water <scp>CO<sub>2</sub></scp> and <scp>N<sub>2</sub>O</scp> Budgets
Bibliographic record
Abstract
ABSTRACT Ditches and canals are omitted from global budgets of inland water emissions, despite research showing them to be emitters of greenhouse gases (GHGs). Here, we synthesize data across climate zones and land use types to show, for the first time, that global ditches emit notable amounts of carbon dioxide (CO 2 ) and nitrous oxide (N 2 O). Ditches had higher per‐area emissions of CO 2 and N 2 O than ponds, lakes, and reservoirs, likely due to high nutrient inputs. Preliminary upscaling showed that the inclusion of ditches would increase global inland water CO 2 emissions by 0.6%–1% and N 2 O emissions by 3%–9%. Trophic state and climate influenced N 2 O emissions, while CO 2 emissions had complex drivers difficult to disentangle at the global scale. This research highlights the importance of including ditches in global inland water GHG budgets and informs more accurate reporting of anthropogenic emissions in national inventories.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".