Testing the DNDC model using N<sub>2</sub>O emissions at two experimental sites in Canada
Bibliographic record
Abstract
Measured data from two experimental sites in Canada were used to test the ability of the DeNitrification and DeComposition model (DNDC) to predict N 2 O emissions from agricultural soils. The two sites, one from eastern Canada, and one from western Canada, provided a variety of crops, management practices, soils, and climates for testing the model. At the site in eastern Canada, the magnitude of total seasonal N 2 O flux from the seven treatments was accurately predicted with a slight average over-prediction (ARE) of 3% and a coefficient of variation of 41%. Nitrous oxide emissions based on International Panel for Climate Change (IPCC) methodology had a relative error of 62% for the seven treatments. The DNDC estimates of total yearly emissions of N 2 O from the field site in western Canada showed an underestimation of 8% for the footslope landscape position and an overestimation of 46% for the shoulder position. The data input for the DNDC model were not of sufficient detail to characterize the moisture difference between the landscape positions. The estimates from IPCC guidelines showed an underestimation of 54% for the footslope and an overestimation of 161% for the shoulder. The results indicate that the DNDC model was more accurate than IPCC methodology at estimating N 2 O emissions at both sites. Key words: Nitrous oxide, DNDC, soil model, greenhouse gas, testing
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".