The identification of sites of biodiversity conservation significance: progress with the application of a global standards
Bibliographic record
Abstract
As a global community, we have a responsibility to ensure the long-term future of our natural heritage.As part of this, it is incumbent upon us to do all that we can to reverse the current trend of biodiversity loss, using all available tools at our disposal.One effective mean is safeguarding of those sites that are highest global priority for the conservation of biodiversity, whether through formal protected areas, community managed reserves, multiple-use areas, or other means.This special issue of the Journal of Threatened Taxa examines the application of the Key Biodiversity Area (KBA) approach to identifying such sites.Given the global mandate expressed through policy instruments such as the Convention on Biological Diversity (CBD), the KBA approach can help countries meet obligations in an efficient and transparent manner.KBA methodology follows the well-established general principles of vulnerability and irreplaceability, and while it aims to be a globally standardized approach, it recognizes the fundamental need for the process to be led at local and national levels.In this series of papers the application of the KBA approach is explored in seven countries or regions: the Caribbean, Indo-Burma, Japan, Macedonia, Mediterranean Algeria, the Philippines and the Upper Guinea region of West Africa.This introductory article synthesizes some of the common main findings and provides a comparison of key summary statistics.
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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 | 0.035 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.023 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".