BORDER CLOSURE: EFFECTS ON THE ONTARIO FEED INDUSTRY
Bibliographic record
Abstract
Border closure leading to restrictions in animal movement and/or ingredients can occur for several reasons. Similar to a foreign animal disease threat, each border closure can assume its own personality and characteristics. As such, it is difficult and perhaps too cumbersome to develop a plan comprehensive enough to cover all possible contingencies. However, stakeholder awareness and contingency strategy development are needed to mitigate the extensive losses possible. One approach to expose the possible challenges involved is to deal with specific situations such as the closure of the border to the movement of over 40,000 pigs weekly from Ontario to the US. This discussion will feature more conjecture than detail. The reason is simple. While there have been real-life examples of border closure and their impacts on agri-business, little has been done to prepare or to establish protocols in the event of other closures. Perhaps it is felt that the Federal Government will provide the leadership required. Based on past experiences with foreign animal diseases (FAD) such as the avian influenza outbreak in the Fraser Valley, it is clear that stakeholders in agriculture have to be more involved and work in partnership with the federal government to reduce the effects of such disasters. It is therefore gratifying to
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".