Increasing lamb survival in Prince Edward Island sheep flocks
Bibliographic record
Abstract
The overall objective of this program of research was to describe patterns in lamb mortality in sheep flocks in Prince Edward Island (PEI), Canada, and to identify the most influential flock-level factors associated with lamb mortality risk. Data were collected retrospectively from 385 lamb post-mortem submissions (2005 to 2014), and prospectively over one year for 51 distinct lambing groups from 37 sheep flocks. The retrospective data revealed the importance of infectious causes of lamb mortality in PEI sheep flocks. The prospective data revealed that the mean group-level lamb mortality was 11.14% (± 6.64%), with 25 groups having lamb mortality greater than 10%, which is considered higher than acceptable. The following management factors most strongly contributed to decreased lamb mortality: using flock management goal-setting; seeking veterinary advice for medical treatment; using benzimidazole-derivative anthelminthics; feeding higher quality forage (higher crude protein, digestible energy, net energy for maintenance, and lower acid detergent fiber), applying visual lamb identification, feeding a coccidiostat to lambs, administering clostridial vaccination to lambs, and avoiding separation of hypothermic lambs from their dams. A history of neurological problems in ewes contributed to increased mortality in lambs. Gastrointestinal parasite infections, and selenium and vitamin E deficiencies, were widespread in the study flocks. These research findings suggest that, to improve lamb survival in PEI flocks, producers should work with their veterinarians to set flock performance goals, enhance the nutritional management of late-gestation ewes, and improve the preventative health management of ewes and lambs, especially related to gastrointestinal parasitism and infectious diseases.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".