Bibliographic record
Abstract
The USDA’s Grain Stocks and Acreage reports to be released on June 30 will provide important fundamental information for the soybean market and influence prices into the critical summer growing season. The stocks report will provide an estimate of stocks held on June 1 and the size of that estimate can be anticipated based on the estimated size of March 1 stocks, imports during the third quarter of the marketing year, and estimates of consumption during the quarter. March 1 stocks were estimated at 1.531 billion bushels and imports during the third quarter were likely near 6 million bushels based on Census estimates of exports in March and April. Based on the soybean crush estimates for March and April in the USDA’s monthly Fats and Oils: Oilseed Crushings, Production, Consumption and Stocks report and the National Oilseed Processors Association (NOPA) estimate for May, the domestic crush during the third quarter of the marketing year was about 487 million bushels, slightly larger than the crush during the same quarter last year. The NOPA crush estimate for May was record large for the month and exceeded the crush of May 2015 by three percent. To reach the USDA projection of 1.89 billion bushels for the year, the crush during the last quarter needs to be about 450 million bushels, or about the same size as the crush last summer.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.017 |
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".