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Record W4393945018 · doi:10.3389/frym.2024.1237746

Mosquitoes: Buzzing, Biting, and Making People Sick!

2024· article· en· W4393945018 on OpenAlexfundno aff
Lina Rodríguez, Juliana Quintero, Catalina González-Uribe

Bibliographic record

VenueFrontiers for Young Minds · 2024
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsBitingMedicineBiologyEcology

Abstract

fetched live from OpenAlex

Mosquitoes—those small irritating insects that fly around and bite us on warm days—can also carry diseases that can make people very sick. Mosquitoes “bite” to suck blood from people and animals to feed their eggs. When a mosquito bites someone, it can also transmit a virus or parasite to that person. Mosquito-borne diseases are common in many parts of the world. Diseases carried by mosquitoes include malaria, dengue, and Zika. In this article, we will explain how mosquitoes transmit diseases and describe the main symptoms of several important mosquito-borne diseases. We will also tell you how both individuals and communities can protect themselves from mosquitoes and prevent these dangerous diseases.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.003
Scholarly communication0.0020.005
Open science0.0000.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0090.006

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 head0.008
GPT teacher head0.272
Teacher spread0.264 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2024
Admission routes1
Has abstractyes

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