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Record W7115196206 · doi:10.5281/zenodo.17925266

Ancient Mosquitoes: Evolution of Hematophagous Insects and the Emergence of Modern Disease Vectors

2025· article· W7115196206 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typearticle
Language
FieldArts and Humanities
TopicPaleopathology and ancient diseases
Canadian institutionsnot available
Fundersnot available
KeywordsVector (molecular biology)Evolutionary ecologyNatural selectionPhylogeneticsTaxonomy (biology)Anopheles

Abstract

fetched live from OpenAlex

Mosquitoes are dipteran insects belonging to the family Culicidae with an evolutionary history exceeding 100 million years. Fossil evidence from the Cretaceous period demonstrates that ancestral mosquitoes coexisted with dinosaurs and early vertebrates. This article examines the evolutionary trajectory of mosquitoes through an integrated framework of paleobiology, taxonomy and vector biology, emphasizing the critical distinction between hematophagy (blood-feeding behavior) and vector competence (the ability to transmit pathogens), which emerged later in evolutionary history. Fossil mosquitoes preserved in amber from Myanmar, Canada and Eastern Europe reveal early development of blood-feeding mouthparts, yet no definitive evidence of disease transmission. Subsequent genetic adaptations including salivary anticoagulants, olfactory specialization and host selection combined with pathogen co-evolution and environmental changes associated with human settlement and climate, facilitated the emergence of modern disease vectors such as Aedes, Anopheles and Culex. This study argues that mosquitoes are not inherently antagonistic to humans, but rather represent the outcome of complex evolutionary processes within ecosystems increasingly shaped by human activity.

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)0.000
metaresearch head score (Gemma)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.231
Teacher spread0.209 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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