Assessment of the pathogen abatement effects of nutrient management policy: the Ontario Nutrient Management Act, 2002
Bibliographic record
Abstract
Nutrient management strategies and regulations provide for the optimal management of waste materials containing nutrients that may be applied to the land. They are enacted to protect water sources while maximizing the economic and biological value of the nutrients. The Province of Ontario has enacted a new Nutrient Management Act (2002), the purpose of which is to enable the province to enact regulations that establish standards for the management of nutrients. Livestock waste contains not only nutrients, but also many pathogenic microorganisms such as viruses, bacteria and protozoa. Although these contaminants are abundant in livestock waste, no legislation has been specifically designed for their control; instead, nutrient management policies are assumed to be proxies for pathogen management. Therefore, the question is, will nutrient management policies that have been designed specifically to control nutrients also ensure a safe drinking water supply through the control of pathogens?...[This research suggests that]... the ability of pathogens to survive and be transported in numerous environments leaves an uncertainty in the effectiveness of the land application regulations at reducing the risk of pathogen contamination.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".