The stock recovery rate in a Central African rain forest: an index of sustainability based on projection matrix models
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Bibliographic record
Abstract
The stock recovery rate is used in most natural forests of the Congo Basin to assess logging sustainability. This rate is computed using the so-called Dimako formula. Although this formula has been used for many years now in management plans, its mathematical properties have not been closely reviewed. We show that the Dimako formula corresponds to a Leslie matrix model, and then we propose an extension of it as a Usher matrix model. The stock recovery rate at the end of the first felling cycle for six commercial species in the Central African Republic varied between 21.7% and 99.9%. As felling cycles follow each other, the stock recovery rate converged towards a limit that is the asymptotic stock recovery rate. This limit varies between 27.2% and 158.4% for the same six species. Comparing felling scenarios reveals that increasing the minimum harvest diameter was as efficient at increasing the stock recovery rate at the end of the first felling cycle as decreasing the logging intensity. The results for the other parameters of the felling scenarios varied among species, with changes in the stock recovery rate ranging from 0% to 180% at the end of the first felling cycle, and changes in the asymptotic rate ranging from 0% to 685%.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it