Bibliographic record
Abstract
Population growth and general economic performance drives global demand for food and agricultural products, which lays the foundation for trade and U.S. exports (ERS a). Through the effects on employment, purchasing power and income, agricultural exports play a significant role in both the farm and nonfarm economy (Edmonson). In 2006, each export farm dollar earned generated an additional $1.65 in business activity in 2006 (Edmondson). As a result, the $71.0 billion earned in agricultural exports stimulated an additional $117.2 billion in general economic activity in 2006 (Edmondson). Over the past five years, values of agricultural exports from the U.S. have been on the rise hitting record levels (Brooks). Increased demand in Canada and Mexico are primarily responsible for the renewed growth within agricultural exports (Brooks). Figure 1-1 shows all major agricultural products being exported from the U.S. over the past five years. The largest area of agricultural exports from the U.S. has consistently been cereal products. These types of products include major cereals such as barley, millet, and oat, as well as pseudo cereals that include buckwheat, amaranth and quinoa (Seibel). These products currently compose 23% of total U.S. agricultural exports and have traditionally been the largest export product(ERS a).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.145 | 0.084 |
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".