THE GLOBAL PORK SITUATION – WHERE DOES CANADA FIT?
Bibliographic record
Abstract
Pork production is global in nature with trade occurring relatively freely around the world. Most countries now have access to the same genetics and have similar productivity potential. Food safety regulations and health protocols are standard requirements. Industry shocks such as disease (i.e. H1N1) or feed shortages that occur in one country have the ability to affect markets around the world. It is speculated that the main differentiating factors in global pork production are housing standards/animal welfare issues, producer management ability and government policy (i.e. environmental standards, financial support, trade barriers). Canadian pig producers have faced many challenges during the last few years due to a stronger Canadian dollar, country of origin labelling, feed cost volatility, low market prices, H1N1, and so on. As a result, there has been and will continue to be attrition of producer numbers. Amid these challenging times however it is important to assess the attributes of the Canadian industry, to find light at the end of the tunnel for those with the stamina to remain in the business. What strategies will be needed to set Canada apart from other pork producing countries in the future and to be competitive at a global level?
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".