Weekly Outlook: Soybean Stocks, Acreage, and Weather
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 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 | 0.000 |
| 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 it