Effects of Competition and Space on Country Elevator Grading Practices and Prices for Wheat
Bibliographic record
Abstract
of Work A simulation analysis identifies optimal wheat grading and pricing strategies for country elevators under three possible competitive structures.The three competitive structures are: I) an elevator is a perfect monopsony, with no competition in its potential trade area~2) an elevator has competitors that do not follow its lead in formulating a grading and pricing strategy~and 3) an elevator has competitors that copy its grading/pricing strategy.For each structure, an elevator and its competitors consider three possible grading and pricing strategies: 1) an elevator grades and segregates the wheat delivered, and also pays producers prices that differ according to the quality of wheat they deliver~2) an elevator grades the wheat received and segregates it to receive prices from next-in-line (Nll.)buyers that are adjusted for quality, but pays producers one price for all qualities ofwheat~and 3) an elevator does not grade the wheat received, nor does it segregate the wheat into different qualities or pay different prices to producers for different qualities.A sensitivity analysis identifies the optimal strategies over a range of reasonably likely operating environments, consistent with the range of conditions observed during the 1995, 1996, and 1997 harvests.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".