Conserving Genetic and Genomic Diversity in Accordance with the Global Biodiversity Framework
Bibliographic record
Abstract
Adopted in December 2022, the Kunming–Montreal Global Biodiversity Framework (KMGBF) under the Convention on Biological Diversity outlines a visionary road map guiding humanity's relationship with nature. KMGBF commitments require active intervention, sustained monitoring and scientific reporting, capacity building for tools and technologies, and cooperation among 196 signatories. Genetic diversity, which underlies adaptation and fitness, is a core tenet of the KMGBF. This article aims to distill the KMGBF to help researchers, practitioners, and other interested parties achieve its commitments. In five sections, we address ( a ) the KMGBF's terminology and scope, ( b ) the intersection of KMGBF targets with genetic diversity, ( c ) genetic monitoring for tracking its progress, ( d ) paradigms and decision frameworks to guide genetic conservation actions, and ( e ) emerging frontiers. A better understanding of the KMGBF will help researchers, practitioners, and other interested parties more effectively engage and fulfill global, national, and local commitments to the conservation of our planet's biodiversity.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".