Human outbreak detection and best practice MPXV analysis and interpretation with squirrel
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
High numbers of reported mpox cases and recent identification of multiple sustained human outbreaks of mpox virus (MPXV) have highlighted the need for robust, best-practice genomic surveillance tools. In light of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, many labs across the globe developed the capacity to do virus genome sequencing; however, MPXV presents additional analytical challenges due to its large genome size, tracts of low-complexity or repeat regions, genetically distinct clades, and the need to perform bespoke apolipoprotein B mRNA editing enzyme catalytic polypeptide-like 3 (APOBEC3)-mutation reconstruction. We present squirrel (Some Quick Reconstruction to Resolve Evolutionary Links), an open source bioinformatic tool that can perform clade-aware alignment, mutation quality assessment, phylogenetic inference, and automated APOBEC3-mutation classification on branches of the phylogeny. Squirrel can be run on the command line or launched through the EPI2ME graphical user interface through the squirrel-nf workflow, enabling robust analysis without need for the command line. With the interactive output report produced and publication-ready APOBEC3-reconstruction visualization, squirrel enables researchers to distinguish between zoonotic and sustained human outbreaks and help accurately inform public health responses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it