A Simple Tactic for Purchasing French Oak Barrels
Bibliographic record
Abstract
This paper identifies a cost-minimizing tactic for U.S. wineries when buying French oak barrels. An otherwise trivial decision is potentially confounded by wineries’ order timing choice and exchange rate risk. Using an intuitive model, which applies exchange rate futures prices and sixteen years of price data for a specific custom-made French oak barrel, we provide a general decision rule. The simple strategy of taking the early order discount dominates all alternative strategies and would have saved wineries nearly $78,000 compared to the more traditional ordering timeframe. Interestingly, using futures market prices does not improve the cost savings. “In wine, there’s truth.” Pliny the Elder, Natural History Neither do men pour new wine into old wineskins. If they do, the skins will burst, the wine will spill, and the wineskins will be ruined. Instead, they pour new wine into new wineskins, and both are preserved." Matthew,9:17.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".