Dimensionless numbers for the sustainable harvesting of a monospecific uneven-aged forest
Bibliographic record
Abstract
This study proposes a simple and direct method based on dimensionless numbers to provide reliable approximations of the population growth rate, the “sustainable/stable” harvest rate, the proportion of trees that has to remain unharvested to retain the stable diameter distribution, and the stable diameter distribution of a forest stand. Those numbers, obtained under conditions of stable equilibrium from a matrix model, could also serve to estimate boundaries between sustainable and unsustainable harvesting. To exemplify and test the results, the model uses data from uneven-aged managed Pinus nigra Arnold stands, considering three levels of tree diameter growth, six levels of basal area, and 33 levels of recruitment, creating a total of 594 planning scenarios. The best approximation of all the variables observed occurred in any case for the scenarios with the lowest level of diameter growth, the lowest level of basal area, and the highest recruitment level. Furthermore, the study reveals the existence of a strong positive linear correlation between those variables and their respective approximations, as well as a small distance between the stable diameter distribution of the stand and its approximation. Finally, we incorporate natural disturbances into the dimensionless numbers and criteria.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".