The Earth BioGenome Project Phase II: illuminating the eukaryotic tree
Bibliographic record
Abstract
#### Sequencing the genomes of eukaryotic species through inclusive, global collaboration Discover the next phase of the [Earth BioGenome Project (EBP)](https://www.earthbiogenome.org/), which aims to sequence the DNA of all known eukaryotic species to protect biodiversity, improve global health, and drive scientific innovation. In their [Frontiers in Science lead article](https://www.frontiersin.org/journals/science/articles/10.3389/fsci.2025.1514835/full), the EBP leaders reveal a refined strategy to scale up the sequencing of 150,000 species. Thanks to major technical advances, high-quality genomes can now be produced 10 times faster and at significantly lower cost. Hear the authors discuss how EBP’s next phase will accelerate biodiversity research, support global conservation, and extend genomic benefits to underserved regions using mobile sequencing labs. Alongside a panel of fellow experts, they will explore the importance of open data sharing, training local scientists, and sequencing at the source—ensuring inclusivity, capacity-building, and benefit-sharing, especially in the Global South. If you would like to join, [register here](events.frontiersin.org/earth-biogenome-project/cassyni).
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.016 |
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".