Emergence of potentially disinfection-resistant, naturalized Escherichia coli populations across food- and water-associated engineered environments
Bibliographic record
Abstract
The Escherichia coli species is comprised of several 'ecotypes' inhabiting a wide range of host and natural environmental niches. Recent studies have suggested that novel naturalized ecotypes have emerged across wastewater treatment plants and meat processing facilities. Phylogenetic and multilocus sequence typing analyses clustered naturalized wastewater and meat plant E. coli strains into two main monophyletic clusters corresponding to the ST635 and ST399 sequence types, with several serotypes identified by serotyping, potentially representing distinct lineages that have naturalized across wastewater treatment plants and meat processing facilities. This evidence, taken alongside ecotype prediction analyses that distinguished the naturalized strains from their host-associated counterparts, suggests these strains may collectively represent a novel ecotype that has recently emerged across food- and water-associated engineered environments. Interestingly, pan-genomic analyses revealed that the naturalized strains exhibited an abundance of biofilm formation, defense, and disinfection-related stress resistance genes, but lacked various virulence and colonization genes, indicating that their naturalization has come at the cost of fitness in the original host environment.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".