Biosecurity in agriculture [electronic resource] : a suggested strategy for the protection of source water against pathogenic contamination
Bibliographic record
Abstract
To reduce the threat of pathogenic responses in humans, the Government of Ontario has introduced the Clean Water Act. The Act is intended to identify, characterize, and mitigate risks to vulnerable sources of drinking water. Applying the appropriate level of protection in those areas where land use activities contribute to the contamination of source water can be achieved through the use of biosecurity strategies comprised of operational measures to treat manure prior to storage and handling. Recent outbreaks of waterborne disease linked to manure management practices has resulted in an increased awareness of the potential risks that livestock operations pose to source water quality. This investigation demonstrated that currently available treatment technologies can significantly reduce pathogen concentrations in livestock manure; however the extent that these measures can be integrated into the proposed Clean Water Act is limited by the lack of controlled, replicated studies conducted at the commercial-scale.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.204 | 0.071 |
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".