Growth and yield of tropical moist forest for forest planning: an inquiry through modeling
Bibliographic record
Abstract
For the forest growth and yield calculations presented here, the process-based model FORMIX was applied in combination with a geographic information system. The study was carried out for the 55 084 ha of the Deramakot Forest Reserve, which consists of heavily logged and degraded lowland dipterocarp forest. FORMIX was used to assess forest development with and without timber harvesting. Three scenarios were compared: undisturbed forest growth and two reduced-impact timber harvesting scenarios ("textbook" and "borderline"). The latter differ in the number of seed trees retained after harvesting. The simulations reveal that timber harvesting is feasible, although stands are presently degraded. Achievable harvesting levels differ widely from 200 to 10 000 m3·year1for the next 40 years for the textbook and borderline scenario, respectively. A future increase up to 80 000 m3·year1is possible if all silvicultural and management standards are strictly observed. An analysis of stand development reveals that the portion of commercial dipterocarps decreases with time, while the portion of pioneer and understorey species increases. Future stands do not contain as many trees >60 cm DBH (diameter outside bark at 1.3 m height above ground), whereas they contain more trees in lower diameter classes, ensuring adequate potential for future harvests.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".