Gaps in the global protection of terrestrial genetic diversity
Bibliographic record
Abstract
Abstract In recent decades, increased anthropogenic impact has led to a global decline in genetic diversity. Before the Kunming-Montreal Global Biodiversity Framework (2022), the absence of international consensus on how to directly assess and monitor genetic diversity, hampered large-scale conservation efforts. Scarcity of assessable genetic data has hindered the evaluation of conservation policies in safeguarding genetic diversity. This study presents the first global approach for evaluating the protection of genetic diversity. By examining the global distribution of mammalian intraspecific mitochondrial DNA and protected area coverage, we identify regions with high genetic diversity and insufficient protection coverage, e.g. regions of critical importance for biodiversity in the Brazilian Atlantic Forest. Additionally, we estimate the impact of global change scenarios on genetically diverse regions with a low degree of protection, revealing high vulnerability of areas in Central Africa. Nonetheless, integrating robust analysis into conservation planning remains challenging. Incorporating Macrogenetics into conservation planning holds the potential to reverse biodiversity decline.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".