Enhanced‐Efficiency Fertilizers for Use on the Canadian Prairies
Bibliographic record
Abstract
In spite of the management practices adopted by Canadian producers to mimize fertilizer losses, N‐use efficiency is normally estimated to be less than 50% in the year of application. Various types of enhanced‐efficiency fertilizers such as nitrification inhibitors, urease inhibitors, and coated N fertilizers are available that attempt to address the constraints associated with traditional N management in order to improve N‐use efficiency and/or the operational efficiency of Canadian agricultural systems. Enhanced‐efficiency N fertilizers can chemically or physically influence the movement and transformations of N in order to improve synchrony between nutrient supply and crop uptake, reduce nutrient losses, and improve nutrient‐use efficiency. Pathways and magnitude of N loss are influenced by soil characteristics, weather conditions, and crop management practices, as well as by fertilizer source and management practices. Therefore, the effectiveness of the various enhanced‐efficiency fertilizers will be strongly dependent on the environmental conditions that influence potential losses. Under environmental conditions where the potential for N loss is high, enhanced‐efficiency fertilizers may provide an effective method of improving N use, particularly where other agronomic factors are optimized so that the crop is able to convert the N supplied into usable yield with the greatest efficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".