Estimating the value of wood quality information in constrained optimization
Bibliographic record
Abstract
In recent years, forest information has been evaluated increasingly through its value in decision making, not solely through its statistical accuracy. The value of forest information is rooted in the ability to make better decisions with better data. When the adopted option differs from the optimal, it incurs suboptimality losses, defined as the difference between the outcome (typically NPV) of the optimal and selected options. In this study, we analyse the value of timber quality information for the timber buyer selecting stands to be purchased with a given budget or demand constraint. In the basic constrained linear programming approach, the option selected as optimal with erroneous data may prove to be infeasible when evaluated with error-free data. To properly estimate the value of information, the costs of violating the constraints need to be included. We present a stochastic goal programming approach for solving this problem in which the violations are penalized with the interest of a loan, in the case of budget constraint, and with diminishing revenues, in the case of demand constraints. We show that information on timber quality has value to the buyer, increasing with the penalty. The value varied from 0 to about 80 €·ha–1, assuming only the timber quality assessments to be uncertain. Using the stochastic solution instead of expected value solution also has value for the buyer.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".