A simple generalization of the Faustmann formula to tree level
Bibliographic record
Abstract
The economic decision model serving as an objective function in forest economics was conceived originally by Faustmann at the stand level. Nevertheless, the tree level seems to be an appropriate scale for analysis, especially for harvesting decisions and certain estimations both at tree and stand levels. However, the Faustmann formula cannot be directly applied to the tree level. The present research has provided certain tree-level formulations of the Faustmann formula, including, in particular, tree expectation value (TEV) and land expectation value (LEV). TEV and tree-level LEV formulas were developed by analyzing the Faustmann formula under deterministic conditions. Unlike previous tree-level decision models presented in the forest economics literature, TEV and tree-level LEV formulas incorporate the expectation value of the land occupied by trees and its variability over time as well as the interaction between trees and their trajectories (cutting age). The proposed formulas were then compared with the Faustmann formula using the first-order condition of optimal harvest age. The TEV and tree-level LEV formulas appeared to be absolutely compatible with the Faustmann formula. The utility of the proposed formulas was then illustrated with application examples, including target diameter, stand expectation value, TEV, LEV, and the value of damage to beech trees or stands in northeastern France.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".