The Canadian pork industry and COVID‐19: A year of resilience
Bibliographic record
Abstract
Abstract While COVID‐19 had the potential to be extremely disruptive to the Canadian pork supply chain, the sector showed resiliency by adjusting to market changes to ensure industry continuation. Unlike other non‐agricultural firms that were mandated to close at times, the pork sector was deemed an essential service and allowed to continue operating throughout the pandemic. Evidence of this resiliency is seen in three main ways. First, market access to the United States was maintained for both live pigs and pork exports. Second, Canada not only maintained market share in global pork exports, but it also actually increased shipments because of strong demand from China caused by African swine fever. Third, the challenges of processing plant closures and labour shortages were overcome in a variety of ways including increasing interprovincial shipments and increasing live pig exports to the United States. Pork consumption on a per capita basis continued the historical downward trend, and it is expected that consumers will return to their normal consumption patterns (e.g., dining at restaurants) despite job losses. At the meat processing level, it is anticipated that there will be an acceleration in the process to automate.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".