Quality forecasts: Predicting when and how much markets value higher‐protein wheat
Bibliographic record
Abstract
Abstract Wheat markets stand out among other major crop commodity markets because pricing at the first point of exchange—typically a grain handling facility—is differentiated on specific quality characteristics. Moreover, the premiums and discounts that elevators offer to obtain grain of specific quality can be significant. The relative importance of quality premiums and discounts to farm‐level production and marketing decisions demonstrate a need to quantitatively measure and explain factors that affect elevators' wheat‐quality pricing decisions. This study develops an informed expectation model of elevators' quality‐based pricing strategies and empirically estimates the model from a lengthy dataset of weekly price observations. I find empirical evidence that elevators use linear pricing schedules, but more aggressively discount wheat with protein levels lower than a baseline than reward higher‐protein wheat. The results also indicate that weather characteristics, futures contract price indicators, and USDA Crop Progress reports are contributors to predicting the new crop protein premiums and discounts, and that out‐of‐sample accuracy for predicting how grain elevators will value wheat protein ranges between 70% and 80%.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".