Managed Trade in Non‐traditional Products: Emerging Issues for Canada's Specialised Livestock Sectors
Bibliographic record
Abstract
The specialised livestock industry is poised for excellent growth and development in both Saskatchewan and Canada. … Specialised livestock have the potential to meet market demands in other parts of the world as well as local opportunities for healthy and exotic foods. (Saskatchewan Agriculture and Food, 2000). … there are now 4000 deer farms in New Zealand. They are running 1.8 million animals, in a country somewhat smaller than Saskatchewan! (Haigh 2000). There are domestic and export markets for venison and export offers the best returns. Over 80% of Australian‐produced venison is now exported … They are large markets with limited Australian market penetration because of the current small size of our industry and our inability to provide the volume required (Mackay 2000). …the behaviour of new enterprises follow a very predictable pattern. Unfortunately, despite the highly predictable nature of these issues and problems, the same mistakes are made over and over (McKinna 1999).
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".