Bibliographic record
Abstract
This paper attempts to address the question of how the value of the forest, the land, and the standing timber should be determined under the generalized Faustmann formula when the beginning and ending value of the land may be different. First, the formulas to determine the value of the forest and the land under such a situation were derived. These formulas were then used to separate the value of the trees from that of the forest. A comparison of the correctly determined valuations of the land against those obtained through a frequently used approximation method showed that at interest rates commonly used, the approximation method overestimates the land value and underestimates the value of the standing timber. Sensitivity analyses showed that higher future land value tends to affect the value of the land less at the beginning of the rotation and more at the end. Its impact on the value of the standing timber may or may not be affected, depending on whether the optimal harvest age is affected or not. Higher final harvest value and higher interest rate affect both the value of the land and the value of the standing timber throughout the entire rotation. Lastly, higher regeneration at the beginning of the rotation simply re-allocates the forest value between that of the land and that of the standing timber, reducing the former while increasing the latter.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| 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".