Development of a Canadian Agricultural Nitrogen Budget (CANB v2.0) model and the evaluation of various policy scenarios
Bibliographic record
Abstract
A Canadian Agricultural Nitrogen Budget model was developed to calculate the agro-environmental indicators: Residual soil nitrogen (RSN) and Indicator of Risk of Water Contamination by Nitrogen (IROWC-N) for 3500 polygons of the 1:1 m Soil Landscapes of Canada scale. Residual Soil Nitrogen was calculated for the census years 1981, 1986, 1991, 1996 and 2001. These results were then used in conjunction with climate data to calculate over-winter N loss and its concentration in the drainage water. The main inputs were the acreages, yields and N recommendation rates for major crops, and the types and numbers of livestock. Various coefficients and assumptions were incorporated into the calculations. Validation of the model was carried out using provincial nitrogen sales data, and results showed good agreement between the calculated fertilizer N and the amount of fertilizer N sold in each province in 1996 and 2001. The two indicators were linked to outputs of the economic-based Canadian Regional Agricultural Model in order to assess the impacts of policy scenarios on nitrogen balance. At the national scale, the scenario of improved N fertilization practices reduced the RSN by 13%. RSN was also sensitive to the N2O:N2 ratio resulting from N losses through denitrification. Key words: Landscape nitrogen model, Agri-Environmental Indicator, Soil Landscapes of Canada, Census of Agriculture
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".