Grain Transportation Report, May 12, 2022
Bibliographic record
Abstract
U.S. exports of distillers' dried grains with solubles (DDGS), a co-product of ethanol production, are a key driver of transportation demand.DDGS are the second-largest U.S. containerized grain export.Total DDGS exports in 2021 were the third-highest on record by volume and, at $3 billion, the highest on record by value, as both shipments and prices of DDGS rose last year.Canada and China showed strong growth in DDGS imports, though Chinese imports were limited by antidumping and countervailing duties.This article reviews DDGS exports for 2021 and first quarter 2022.It also examines the drivers of these exports and the resulting impacts on transportation demand. DDGS Production and ExportsBecause DDGS derive from ethanol, strong ethanol production in 2021 supported DDGS production.U.S. exports of DDGS rose to 11.6 million metric tons (mmt) in 2021, the thirdhighest volume on record.The top five importers-Mexico, Vietnam, South Korea, Indonesia, and Turkey-received 57 percent of total U.S. DDGS exports.Canada and China showed strong growth in demand, together accounting for 10 percent of U.S. DDGS exports in 2021.From 2020 to 2021, total DDGS exports to Canada rose 88 percent.Last summer, a drought in western Canada significantly reduced the country's barley and wheat supply.With the resulting ingredient deficit for cattle and swine feed, Canada's demand for U.S. corn and DDGS rose.Total first-quarter 2022 DDGS exports were 12 percent higher than the same period last year and 10 percent above the 3-year average (fig.1).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".