The impact of COVID‐19 on the grains and oilseeds sector: 12 months later
Bibliographic record
Abstract
Abstract Brewin (2020) was optimistic about the fate of the Canadian grains and oilseeds sector in 2020 as the COVID‐19 pandemic descended on the world. The sector did generate a large crop and, towards the end of 2020, saw a lift in prices. This contributed to record farm income in Canada in 2020. The pace of grain and oilseed exports in Canada and ethanol demand in the east were affected by COVID‐19, but the forecast of a “near normal” 2020 was relatively accurate. Production and prices stayed on track, largely because the world did not impose significant new barriers to trade in cereals and oilseeds and because these sectors have distanced labor in virtually every step of the supply chain which protected these markets from this pandemic. The dominant price factor for the sector remains global demand that had been growing before 2020 relative to the pace of production and may have been stimulated by deficit budgets around the world. Compared to the tight global stocks, COVID‐19 had a minor impact on grain prices which led to steady production worldwide and in Canada. We are still waiting for more evidence to assess the role of federal coordination in the success of the grains and oilseed sector in 2020, but Canada's past participation in trade and safety protocols based on science allowed the grains and oilseed sector in Canada to earn a very good income in 2020.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".