Setting the Upset Price in British Columbia Timber Auctions
Bibliographic record
Abstract
SUMMARY An important element of timber auctions is the upset—the minimum acceptable price, often called the reserve price in other auction environments. The upset has three main purposes: (1) to guarantee substantial revenue in auctions where competition is weak but the upset is met, (2) to limit the incentive for—and the impact of—collusive bidding, and (3) to provide useful information to bidders. We analyze the determination of the upset in British Columbia timber auctions. Setting the upset too high results in unsold stands and produces an upward bias in price if the competitive auctions are used to determine stumpage rates for non-auctioned timber. Setting the upset too low will reduce auction revenue and can create downward bias when the auction prices are used to calculate timber prices for non-auctioned stands. It is therefore important to set the upset at or near the optimal level. We present the theory of upset pricing and then apply that theory to the data available from historical timber auction sales in the BC Interior from 1999 to 2000. We find that an upset of about 70 percent (a rollback of 30 percent) maximizes auction revenues if the Ministry values timber at about 52 to 56 percent of its appraised value. This upset strikes the right balance between enhanced revenues and unsold timber stands. Given its importance, this upset calibration should be refined as additional data becomes available to assure that the upset is not set too high or too low.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.032 | 0.003 |
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; both teacher heads agree on what is shown here.
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".