Intercepting pandemics through genomics
Bibliographic record
Abstract
Ecological interactions that cross domains of life have major impacts on ecosystems and human health. Although the coronavirus disease 2019 (COVID-19) pandemic makes this point with destructive clarity, it is clear that zoonotic pathogens pose a standing threat to our species as demonstrated by Ebola, Middle East respiratory syndrome (MERS), and severe acute respiratory syndrome (SARS). Other species experience similar pandemics and are both sources of, and sensitive to, shared pathogens. More than a century ago, Cryphonectria parasitica ( Left ), the fungus that causes Chestnut blight, devastated the American Chestnut tree. Until its eradication in 2011, Rinderpest morbillivirus ( Right ) decimated cattle and other ungulate populations. A biosurveillance system would help prevent future deadly and economically devastating pathogen outbreaks. Image credit: Paul Beales/UK Crown Copyright ( Left ); © Bioimaging at The Pirbright Institute ( Right ). Hence, there is an urgent need to establish a global, genomic-based biosurveillance platform, a development which would be of immense value to biosecurity, biodefense, and the economy. If implemented, this “pandemic interception system” would hugely advance our understanding of the natural world. Three major research programs are poised to support this effort: BIOSCAN, the Earth BioGenome Project (EBP), and the Global Virome Project (GVP). Each of these global programs is now working to develop approaches in comparative genomics that are needed to discover all species and to reveal their interactions. The diversity of infectious agents involved in host–pathogen interactions needs immediate clarification, especially with regard to those agents that transfect phylogenetically divergent lineages. Such information will enable a surveillance system that facilitates preemptive strikes and rapid responses to outbreaks as well as early development of diagnostic pipelines and vaccines. If pursued with a tiny fraction of the resources devoted toward the suppression of COVID-19, rapid progress could be made in identifying every pathogen hosted by birds and mammals … [↵][1]1To whom correspondence may be addressed. Email: kressj{at}si.edu. [1]: #xref-corresp-1-1
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".