Sustainable Forest Management Practices and West Malaysian Log Market
Bibliographic record
Abstract
The global demand for timber products will continue to grow in line with increasing population and economic development especially in many developing countries. Simultaneously, forest ecosystem plays important roles in the environmental services such as carbon sequestration, recreational, water catchments, wildlife reserve and soil protection. Therefore, Malaysia is committed in implementing Sustainable Forest Management (SFM). The forest are harvested in a sustainable manner by adopting the method of reduce impact logging practices. This resulted to diminishing supply of logs to downstream timber industry. The West Malaysian log supply is in deficit since 1995. This has significant impact on major timber products. They have moved from resources surplus to one of deficit in Malaysia. Therefore, it is interesting to know the behaviour of West Malaysian log market with the implementation of SFM policy. The results indicate that full adoption of SFM could lead to substantial reduction of supply. Furthermore, a sustained price increase in the long run does not seem to have significant impact on the demand side. In conclusion, the ongoing adaptation of West Malaysian forestry to the standards of the SFM certification programs could have substantial effects only on the log supply. This will probably influence the scheme of forest plantation establishment in sustaining the West Malaysian forest sector.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.008 | 0.001 |
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".