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Record W4393141835 · doi:10.1073/pnas.2310859121

Pathogen prospecting of museums: Reconstructing malaria epidemiology

2024· article· en· W4393141835 on OpenAlexafffund
Mark P. Nelder, Rachel Schats, Hendrik N. Poinar, A. M. Cooke, Megan B. Brickley

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2024
Typearticle
Languageen
FieldMedicine
TopicMalaria Research and Control
Canadian institutionsMcMaster UniversityPublic Health Ontario
FundersNederlandse Organisatie voor Wetenschappelijk OnderzoekCanada Research Chairs
KeywordsMalariaVector (molecular biology)BiologyPlasmodium falciparumProspectingPathogenCoevolutionInsecticide resistancePlasmodium (life cycle)Plasmodium yoeliiEcologyParasite hostingImmunologyGeneticsToxicology

Abstract

fetched live from OpenAlex

Malaria is a disease of global significance. Ongoing changes to the earth’s climate, antimalarial resistance, insecticide resistance, and socioeconomic decline test the resilience of malaria prevention programs. Museum insect specimens present an untapped resource for studying vector-borne pathogens, spurring the question: Do historical mosquito collections contain Plasmodium DNA, and, if so, can museum specimens be used to reconstruct the historical epidemiology of malaria? In this Perspective, we explore molecular techniques practical to pathogen prospecting, which, more broadly, we define as the science of screening entomological museum specimens for human, animal, or plant pathogens. Historical DNA and pathogen prospecting provide a means of describing the coevolution of human, vector, and parasite, informing the development of insecticides, diagnostics, therapeutics, and vaccines.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.003
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.082
GPT teacher head0.374
Teacher spread0.292 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations8
Published2024
Admission routes2
Has abstractyes

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