Bibliographic record
Abstract
Abstract While downstream distribution and demand is likely to be hampered by the labor and income effects of COVID‐19, Canada is expected to produce over 88 million tons of grains and oilseeds in 2020. Canadians have valid concerns about delays related to their changing needs as millions move their purchases from food services to retail groceries, but they should not worry about our overall supply of calories. Despite some shortages, the supply chains for flour and cooking oil are not likely to be blocked for an extended period. Learning from the coordinated needs of the BSE crisis in the beef sector, the federal government developed Value Chain Roundtables in 2003, including one for grains. These roundtables bring together government and industry to tackle the issues that face each sector's major needs, including food safety, transportation infrastructure, and market access. A working group made up of various roundtable members was set up specifically to deal with COVID‐19‐related supply chain challenges. This gives both industry and government a venue to attack any choke point or breakdown within our agrifood supply chains—the exact response we need at this time. A preestablished forum for discussion of critical issues at these roundtables, assuming the right players are active and present, cannot hurt, but it would useful for future planners and researchers if the federal government could clarify any positive impact they have.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".