Quantification and estimation of nitrous oxide emissions from dairy manure applications in a western Quebec pea-forage and an eastern Ontario alfalfa-forage cropping system : by Lynda G. Blackburn.
Bibliographic record
Abstract
Agricultural systems are known to emit nitrous oxide (N2O)---a potent greenhouse gas. The roving flux tower measuring system of Agriculture and Agri-Food Canada was used to make continuous measurements of N2O fluxes in an edible pea field in Western Quebec in 2003-04 and then in an alfalfa-timothy forage field in Eastern Ontario in 2004-05. The experiment was designed to capture, at the field scale, the expected large N2O emissions occurring as a result of fertilizer application for a year, in relation to both large precipitation events and spring thaw. Growing season N2O emissions averaged 0.5 to 5 mg N2 O-N m-2 d-1 with peaks following snow melt (between 5 and 8 mg N2O-N m-2 d-1) and manure applications (8 to 37 mg N2O-N m-2 d -1). Although generally small (<0.25 mg N2O-N m -2 d-1), emissions were detectable during the fall and winter, indicating the importance of including them in annual emission totals. The measurements were used to verify the performance of the simulation model DNDC (DeNitrification-DeComposition) in estimating N2O emissions from legumes and in response to dairy manure application. Sensitivity tests were also carried out in which baseline input values were modified. Results suggest that the current model version (DNDC8.9) requires further modification prior to application for estimating greenhouse gas emissions in national accounting systems.
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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 | 0.000 |
| 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".