Enabling Africa’s implementation of the Kunming-Montreal Global Biodiversity Framework through the African digital sequence information data bank
Bibliographic record
Abstract
The African BioGenome Project (AfricaBP) is a Pan-African effort aimed at sequencing the genomes of 105,000 African endemic and indigenous species to support food systems, conservation, and ensure data-sharing and equitable benefits. This effort aligns with the Kunming-Montreal Global Biodiversity Framework (KMGBF), which aims to prevent or mitigate biodiversity loss while facilitating equitable access and benefit-sharing from genetic resources and Digital Sequence Information (DSI) and securing adequate technical and scientific cooperations. The AfricaBP Open Institute for Genomics and Bioinformatics (AfricaBP Open Institute) is the knowledge exchange programme of the AfricaBP which aims to overcome infrastructural barriers through the development of technology and infrastructure. A key component of AfricaBP Open Institute's vision is the establishment of the African Digital Sequence Information Data Bank for Biodiversity and Agriculture (African DSI Data Bank), a federated platform for storing, analyzing, visualizing and sharing genetic data across the African continent. The African DSI Data Bank will address the current fragmentation of DSI across African institutions by linking existing databases and resources while ensuring compliance with regional and global standards. It will use a federated model, leveraging existing (and new) infrastructures across Africa, that allow institutions and countries to retain data sovereignty while adhering to national, regional, and international access and benefit-sharing regulations. Through a proposed Global Access Point (GAP), researchers will be able to gain equitable access to sequence data and genomic metadata via a decentralized network. Furthermore, to understand the current landscape of biodiversity and agricultural DSI databases, analyses, visualization, and data sharing platforms, AfricaBP Open Institute conducted a survey across Africa, and recorded 161 responses. Although the majority of these participants shared common challenges such as limited infrastructure, funding, and capacity building, the overwhelming indication was that they support an African-based DSI platform through an inclusive governance model. Consequently, we describe the proposed roadmap for the creation of an African DSI Data Bank that includes African DSI federated database, visualization, analysis, and sharing platforms, as well as the ethical, legal, social, KMGBF, and sustainability considerations associated with such an infrastructure.
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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 | 0.039 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".