Value of quality information of Scots pine stands in timber bidding
Bibliographic record
Abstract
In any decision-making situation under uncertainty, the decision-maker can either choose between different alternatives with the current information or reduce the uncertainty by collecting more information. The value of the information can be defined as the difference between the expected value of an activity with and without the information. We examined the value of additional information in a case of competitive bidding for a given block of timber. Our main focus was on the uncertainty of roundwood quality, and volume was assumed to be known with certainty. The uncertainty of quality was described with the uncertainty of dimensions of living and dead crown. The prior information concerning the crown dimensions was obtained from statistical models or from an assumed uniform distribution. The value of each tree was calculated by predicting the proportions of different lumber grades and by-products as a function of the dimensions of the stem and the crown. The results showed that, if only uniform distribution was available as prior information, the quality information had a high value for the timber buyer. However, if the prior information from the statistical models was used, investing in quality information was profitable only for the stands with the highest volume.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".