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Record W1798861474 · doi:10.3389/fmicb.2015.01120

The out-of-the-delta hypothesis: dense human populations in low-lying river deltas served as agents for the evolution of a deadly pathogen

2015· article· en· W1798861474 on OpenAlexafffund
Fabini D. Orata, Munirul Alam

Bibliographic record

VenueFrontiers in Microbiology · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVibrio bacteria research studies
Canadian institutionsUniversity of Alberta
FundersUniversité de MontréalAlberta InnovatesAlberta Innovates - Technology FuturesNatural Sciences and Engineering Research Council of CanadaCanadian Institute for Advanced Research
KeywordsVibrio choleraeCholeraPandemicDeltaBiologyEcologyBrackish waterPathogenHuman pathogenGeographyDiseaseCoronavirus disease 2019 (COVID-19)MicrobiologyInfectious disease (medical specialty)BacteriaMedicine

Abstract

fetched live from OpenAlex

Cholera is a diarrheal disease that has changed the history of mankind, devastating the world with seven pandemics from 1817 to the present day. Although there is little doubt in the causative agent of these pandemics being Vibrio cholerae of the O1 serogroup, where, when, and how this pathogen emerged is not well understood. V. cholerae is a ubiquitous coastal species that likely existed for tens of thousands of years. However, the evolution of a strain capable of causing a large-scale epidemic is likely more recent historically. Here, we propose that the unique human and physical geography of low-lying river deltas made it possible for an environmental bacterium to evolve into a deadly human pathogen. Such areas are often densely populated and salt intrusion in drinking water frequent. As V. cholerae is most abundant in brackish water, its favored environment, it is likely that coastal inhabitants would regularly ingest the bacterium and release it back in the environment. This creates a continuous selection pressure for V. cholerae to adapt to life in the human gut.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.297

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.052
GPT teacher head0.303
Teacher spread0.251 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations34
Published2015
Admission routes2
Has abstractyes

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