GRADES/CLASSES OF HARD WHEAT EXPORTED FROM THE UNITED STATES: ANALYSIS OF DEMAND AND TRENDS 1986-2003
Bibliographic record
Abstract
Questions have emerged in the United States and Canada regarding the role and function of quality in international markets. One has been the definition and composition of different market segments, with a particular focus on higher quality customers. In this study, trends in U.S. wheat exports are analyzed by class, grade, protein, and market segment. Analysis shows trends toward increases in exports of higher grades of Hard Amber Durum (HAD) and Hard Red Spring (HRS); whereas, exports of higher quality grades for Hard Red Winter (HRW) appear to be moderate. Exports of HRS, HRW, and HAD show increases in proportion of exports shipped at higher protein levels and increases in the proportion of exports where protein is specified. Cluster analysis for each class indicated there have been changes through time including: dockage levels for the highest quality segments declined, the percent of shipments specifying protein increased, and there is a shift toward more market segments. Shares of export volumes for the highest quality segments for both HAD and HRS more than doubled their share of export volume from 7% to 21% for HAD and 18% to 42% for HRS, while shares for the high quality segment for HRW were similar in size to earlier periods, although two moderate quality segments did emerge that were not present in earlier periods.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".