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Record W4239147603 · doi:10.1093/jmedent/47.5.833

A High Throughput Screening System for Determining the Three Actions of Insecticides Against<i>Aedes aegypti</i>(Diptera: Culicidae) Populations in Thailand

2010· article· en· W4239147603 on OpenAlexaff
Kanutcharee Thanispong, Nicole L. Achee, John P. Grieco, Michael J. Bangs, Wannapa Suwonkerd, Atchariya Prabaripai, Kamlesh R. Chauhan, Theeraphap Chareonviriyaphap

Bibliographic record

VenueJournal of Medical Entomology · 2010
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsEncana (Canada)
FundersNational Institutes of HealthThailand Research FundWorld Health Organization
KeywordsAedes aegyptiBiologyToxicologyPesticideAedesToxicityPopulationVeterinary medicineEcologyLarvaEnvironmental healthMedicine

Abstract

fetched live from OpenAlex

Chemicals can protect humans from the bites of hemophagous arthropods through three different primary actions: irritancy (excitation), repellency, or toxicity, actions that can be evaluated using a laboratory-based assay system. In this study, the deterrent and toxic actions of three synthetic pyrethroids and DDT were characterized on six field strains of Aedes aegypti from Thailand under laboratory-controlled conditions using the high throughput screening system. All six strains showed significant contact irritant responses to the three synthetic pyrethroids, but significantly weaker irritant responses to DDT. Marked repellency responses were seen in all six Ae. aegypti test strains exposed to DDT, whereas the synthetic pyrethroids resulted in greater toxicity than DDT under similar test conditions. Although significantly different in actions, irritancy and repellency may reflect and be influenced by the background insecticide susceptibility status of a particular mosquito population. Results from this study can be used to guide decision making regarding more effective Ae. aegypti adult control in Thailand.

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)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.118
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.348
Teacher spread0.296 · 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 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

Citations10
Published2010
Admission routes1
Has abstractyes

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