Foreign Agricultural Trade of the United States May/June
Bibliographic record
Abstract
Increased world agricultural production and lower prices helped depress U.S. agricultural exports for the first 7 months of fiscal year (FY) 1985(October 1984-Apri1 1985). Increased consumer demand, freeze damage to F10rida f s winter fruit and vegetable crops, and higher prices pushed U.S. farm product imports to $11.8 billion during the first 7 months, a 7-percent gain over year-earlier leve1s. The U.S. dollar fe11 sharp1y during March and April against the five major foreign currencies most important in agricultural trade. U.S. agricultural exports assisted by Commodity Credit Corporation programs tota1ed over $3.6 billion during FY 1984, down 10 percent from FY 1983's record-high of nearly $4.1 billion. Increased U. S., grain exports pushed U.S. agricultural exports to the USSR to over $2.8 billion in calendar year 1984.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.061 | 0.019 |
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".