Bibliographic record
Abstract
Abstract Dealing with E. coli in the early post-weaning period has been an ongoing theme in the swine industry since many years. Many strategies have been putted in place in order to reduce the issue. Knowledge in nutrition and use of specific feed additive have for sure led the way as solution to overcome those enteric challenge. We also saw a shift over the last 10-15 years for an increase in wean age in order to have a more robust and well-developed pig at weaning. Despite those improvements, use of pharmacological dose of zinc in the early phase post-weaning has remain a key strategy to control E. coli. On the other hand, in some case, efficacy of the historical pharmacological dose of zinc of 2000 to 25000 ppm for the first 2-3 weeks post-weaning seem to be not sufficient to control E. coli challenge. Higher dose and/or use over an extended period of time seem to be more common those days. The reason why we are talking more and more about zinc oxide is that many countries, mostly in Europe, have restricted/banded to use of high dose of zinc in any diet for pig. Limit have now been set at level around 150 ppm. Concern about environmental impact, but mostly about link with antibiotic resistance have been the main concern toward the ban of pharmacological dose of zinc. Canada feed regulations is under review and expectation is that we will also have a ban on high dose of zinc. With that in mind, our group have worked toward finding way to successfully feeding pig with low level of zinc and try to limit the impact of E. coli. We have run multiple research projects throughout the year and we are still conducting project that focus on feeding low level of zinc. Our belief and observation are that the success rely on a broader approach vs the use of single additive to replace the zinc oxide. We are talking about nutritional concept that include a complete review of how we approach the formulation of the diets for nursery pigs. Energy and protein level, use of fiber, specific feed additive, feed and water acidification are some of the strategies that need to be reviewed and evaluated in order to have a complete solution and to ensure better rate of success. The presentation will cover our experience throughout the last 10 years with the change that we implemented and that seem to allow for being successful in feeding pig in nursery with lower zinc oxide level.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".