Role of livestock in microbiological contamination of water: Commonly the blame, but not always the source
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Since the 1940s, livestock production practices in North America have evolved from extensive to intensive systems, concentrating animals, nutrients, and their associated microorganisms within limited geographical areas. Livestock wastes can harbor both bacterial and protozoal pathogens, and surface and groundwater contamination has been, but is not always, linked to extensive and intensive livestock operations. In mixed-activity watersheds, fecal contamination can be of livestock, human, or wildlife origin. Fecal indicator microorganisms are not always indicative of the disease risk of water, a limitation that is being overcome by the development of molecular identification methods that specifically target pathogens. Best management manure handling, storage, and application practices can substantially reduce the risk of microbial contamination of surface and groundwater. Livestock management practices can reduce the release of pathogens into the environment. The purity of water can never be fully guaranteed; consequently, a multiple-barrier approach is most efficacious in ensuring the production of pathogen-free drinking water.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it