Biofoundries and citizen science can accelerate disease surveillance and environmental monitoring
Bibliographic record
Abstract
A biofoundry is a highly automated facility for processing of biological samples. In that capacity it has a major role in accelerating innovation and product development in engineering biology by implementing design, build, test and learn (DBTL) cycles. Biofoundries bring public and private stakeholders together to share resources, develop standards and forge collaborations on national and international levels. In this paper we argue for expanding the scope of applications for biofoundries towards roles in biosurveillance and biosecurity. Reviewing literature on these topics, we conclude that this could be achieved in multiple ways including developing measurement standards and protocols, engaging citizens in data collection, closer collaborations with biorefineries, and processing of samples. Here we provide an overview of these roles that despite their potential utility have not yet been commonly considered by policymakers and funding agencies and identify roadblocks to their realization. This document should prove useful to policymakers and other stakeholders who wish to strengthen biosecurity programs in ways that synergize with bioeconomy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".