Centering accessibility, increasing capacity, and fostering innovation in the development of international eDNA standards
Bibliographic record
Abstract
Environmental DNA (eDNA) includes a set of rapidly emerging technologies that have the potential to support environmental monitoring and biodiversity conservation through novel, non-invasive, cost-effective and democratic methods and tools. Meanwhile, eDNA researchers are developing international standards for eDNA technologies, methods and data outputs. For eDNA technologies to be accessible, useful and appropriate, we must ensure that any standards developed include a broad conception of users from around the world, a diversity of ecological contexts and locations and, most importantly, a realistic outlook on research capacities and infrastructure. In this article, we assemble perspectives on international standardisation of eDNA from a diverse and global group of users and experts from Africa, South America and the Pacific Islands. The authors of this article collaborated by answering and discussing a set of open-ended questions aimed at eliciting hopes, concerns and experiences regarding eDNA standards. The result is a set of emergent themes and a generative consensus to highlight the need for the creation of adaptable standards, the development of regional capacity, increased sensitising to data sovereignty and the viewing of standardisation as a global capacity-building activity.
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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 | 0.173 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.065 |
| Scholarly communication | 0.025 | 0.040 |
| Open science | 0.003 | 0.040 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".