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Record W2947098905 · doi:10.1002/oa.2786

Tracing zoonotic parasite infections throughout human evolution

2019· article· en· W2947098905 on OpenAlexfundno aff
Marissa L. Ledger, Piers D. Mitchell

Bibliographic record

VenueInternational Journal of Osteoarchaeology · 2019
Typearticle
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaCambridge Trust
KeywordsBiologyDomesticationViral phylodynamicsEvolutionary biologyZoologyPopulationAncient DNAPhylogenetic treeZoonosisEcologyGeneGeneticsVirology

Abstract

fetched live from OpenAlex

Abstract Parasites are useful pathogens to explore human–animal interactions because they have diverse life cycles that often rely on both as hosts. Moreover, some species are not host specific and are transmitted between animals and humans. Today most emerging infections are zoonoses. Here, we take a specific look at the emergence and re‐emergence of zoonotic parasites throughout hominin evolution and consider evolutionary, cultural, and ecological factors involved in this. We combine genetic studies focused on molecular phylogenetic reconstructions, most often using the ribosomal RNA gene unit and mitochondrial genes from modern parasites, archaeological evidence in the form of preserved parasite eggs and antigens in skeletal and mummified remains, and modern epidemiological data to explore parasite infections throughout hominin evolution. We point out the considerably ancient origins of some key zoonotic parasites and their long coevolutionary history with humans and discuss factors contributing to the presence of many zoonotic parasites in the past and today including dietary preferences, urbanization, waste disposal, and the population density of both humans and domesticated animals.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.015
GPT teacher head0.353
Teacher spread0.338 · 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

Citations43
Published2019
Admission routes1
Has abstractyes

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