How can biodiversity strategy and action plans incorporate genetic diversity and align with global commitments?
Bibliographic record
Abstract
National, subnational, and supranational entities are creating biodiversity strategy and action plans (BSAPs) to develop concrete commitments and actions to curb biodiversity loss, meet international obligations, and achieve a society in harmony with nature. In light of policymakers' increasing recognition of genetic diversity in species and ecosystem adaptation and resilience, this article provides an overview of how BSAPs can incorporate species' genetic diversity. We focus on three areas: setting targets; committing to actions, policies, and programs; and monitoring and reporting. Drawing from 21 recent BSAPs, we provide examples of policies, knowledge, projects, capacity building, and more. We aim to enable and inspire specific and ambitious BSAPs and have put forward 10 key suggestions mapped to the policy cycle. Together, scientists and policymakers can translate high level commitments, such as the Convention on Biological Diversity's Kunming-Montreal Global Biodiversity Framework, into concrete nationally relevant targets, actions and policies, and monitoring and reporting mechanisms.
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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 | 0.070 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.022 | 0.029 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".