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Record W2592922206 · doi:10.1101/106740

<i>Salmonella enterica</i> genomes recovered from victims of a major 16th century epidemic in Mexico

2017· preprint· en· W2592922206 on OpenAlexfundno aff
Åshild J. Vågene‬, Michael G. Campana, Nelly M. Robles García, Christina Warinner, Maria A. Spyrou, Aida Andrades Valtueña, Daniel H. Huson, Noreen Tuross, Alexander Herbig, Kirsten I. Bos, Johannes Krause

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2017
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaMäxi-StiftungMax-Planck-Gesellschaft
KeywordsSalmonella entericaOutbreakEnteric feverIndigenousSalmonellaPopulationSerotypeBiologyGeographyVirologyAncient DNATyphoid feverMedicineEnvironmental healthEcologyGeneticsBacteria

Abstract

fetched live from OpenAlex

Abstract Indigenous populations of the Americas experienced high mortality rates during the early contact period as a result of infectious diseases, many of which were introduced by Europeans. Most of the pathogenic agents that caused these outbreaks remain unknown. Using a metagenomic tool called MALT to search for traces of ancient pathogen DNA, we were able to identify Salmonella enterica in individuals buried in an early contact era epidemic cemetery at Teposcolula-Yucundaa, Oaxaca in southern Mexico. This cemetery is linked to the 1545-1550 CE epidemic locally known as “ cocoliztli ”, the cause of which has been debated for over a century. Here we present two reconstructed ancient genomes for Salmonella enterica subsp. enterica serovar Paratyphi C, a bacterial cause of enteric fever. We propose that S. Paratyphi C contributed to the population decline during the 1545 cocoliztli outbreak in Mexico. One Sentence Summary Genomic evidence of enteric fever identified in an indigenous population from early contact period Mexico.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.223
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations25
Published2017
Admission routes1
Has abstractyes

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