The Evolution of Virulence in Non-O157 Shiga Toxin-Producing Escherichia Coli
Bibliographic record
Abstract
Shiga toxin-producing Escherichia coli (STEC) are zoonotic foodborne and waterborne pathogens that are a serious public health concern because they cause outbreaks and the potentially fatal hemolytic uremic syndrome (HUS). The most common STEC serotype associated with human disease is O157:H7, but there is a growing recognition of over 100 non-O157 serotypes that also may result in human illness. Some of these non-O157 STEC strains cause outbreaks and severe disease such as HUS and hemorrhagic colitis, whereas others are associated with only mild diarrhea or with no human disease at all. The relative scarceness of whole genome sequence data for non-O157 STEC has limited the scientific discovery into the genetic basis of these differences in virulence. Uncovering the scope of genetic diversity and phylogeny of the non-O157 STEC through targeting sequencing of clinically relevant isolates will offer new biological insight to the pathogenic behavior of these emerging pathogens. These approaches would also enable molecular risk assessment strategies to rapidly identify and respond to emerging non-O157 STEC that pose a serious public health risk to humans.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".