Connecting a process-based forest growth model to stand-level economic optimization
Bibliographic record
Abstract
This study extends the economic literature on forest stand management by applying a process-based, rather than empirical, stand growth model. The economics of timber production is investigated using a distance-independent, individual tree process model specified for pure Scots pine (Pinus sylvestris L.) stands. Stem taper and crown morphology information are used for bucking the harvested trees into several roundwood categories according to quality and dimension requirements applied in the Finnish timber markets. Explicit inclusion of causality and timber quality in stand-level economic optimization generates a set of new results. Economic optimization decreases biomass production but increases roundwood production, compared with undisturbed stands. Optimal rotation length is insensitive to changes in the rate of interest beyond 4% owing to nonmonotonic value growth. Better quality attributes and higher productivity in resource use are partial reasons for favoring lower canopy trees in optimal thinnings. The first thinnings are light, irrespective of the rate of interest, because of their favorable feedback effects on the quality of residual trees. Production of the highest-grade roundwood is rational only at rates of interest lower than those prevailing in the capital markets. An example of two optima representing distinct timber management strategies is shown.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".