Bibliographic record
Abstract
For the development of a sustainable agricultural industry, the Government of Manitoba has been continuously improving programs, policies and regulations aimed at the control of diffuse agricultural pollution. Manitoba’s pig industry is one of the most important in Canada and is the most valuable agricultural sector in the Province. However, this industry has been under scrutiny for manure management and its potential implication for the eutrophication of several waterways and waterbodies. One of the challenges of trying to implement regulations or recommendations to control agricultural pollution is to evaluate the economic impacts on the livestock sector. Therefore, the main objective of this project was to propose a framework for the economic evaluation of the impacts of the new phosphorus P regulations. To demonstrate the new regulations, manure application rates were assessed with the help of three different nutrient management options: N-based nutrient recommendations, up to two times crop P2O5 removal and up to one times crop P2O5 removal. Five nutrient management strategies were examined to acquire information on the economic impacts of these new regulations: current land base sufficient for N or P-based annual manure application, current land base sufficient for N-based annual or multi-year
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.583 | 0.492 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".