Key Biodiversity Areas (KBA): An Important Approach in Mainstreaming Biodiversity Conservation in Malaysia
Bibliographic record
Abstract
Abstract Malaysia has adopted two ways of conserving its biodiversity; species-based and landscape-based approaches. However, there are limitations of these approaches that can be addressed via Key Biodiversity Areas (KBA). Hence, the aim of the study is to review and compare several current conservation approaches in Malaysia with KBA. Systematic literature search was done using a set of keywords in search engine and visited official government websites to obtain relevant documentations on conservation and biodiversity in Malaysia. Based on the findings, KBA is a holistic approach consisting of several biodiversity elements, criteria and themes that can be put in place, including Important Plant Areas (IPA), Important Bird and Biodiversity Areas (IBA), Important Fungus Areas and Prime Butterfly Areas. This approach has successfully helped many countries such as the Philippines, Indo-Burma hotspot, Uganda, and Canada identify and prioritize important sites for biodiversity conservation. Thus, KBA approach is an alternative approach to support stakeholders in mainstreaming biodiversity conservation approaches in Malaysia for more effective conservation planning in the future. This approach also offers geographic targets for the expansion of protected area coverage and identifies the site for urgent conservation action.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".