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Record W2606957619 · doi:10.1038/s41598-017-01039-1

A sublethal dose of a neonicotinoid insecticide disrupts visual processing and collision avoidance behaviour in Locusta migratoria

2017· article· en· W2606957619 on OpenAlexafffund
Rachel H. Parkinson, Jacelyn M. Little, John R. Gray

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Saskatchewan
KeywordsNeonicotinoidLocustSensory systemInsectNeuroscienceBiologyImidaclopridAvoidance responsePesticideEcology

Abstract

fetched live from OpenAlex

Neonicotinoids are known to affect insect navigation and vision, however the mechanisms of these effects are not fully understood. A visual motion sensitive neuron in the locust, the Descending Contralateral Movement Detector (DCMD), integrates visual information and is involved in eliciting escape behaviours. The DCMD receives coded input from the compound eyes and monosynaptically excites motorneurons involved in flight and jumping. We show that imidacloprid (IMD) impairs neural responses to visual stimuli at sublethal concentrations, and these effects are sustained two and twenty-four hours after treatment. Most significantly, IMD disrupted bursting, a coding property important for motion detection. Specifically, IMD reduced the DCMD peak firing rate within bursts at ecologically relevant doses of 10 ng/g (ng IMD per g locust body weight). Effects on DCMD firing translate to deficits in collision avoidance behaviours: exposure to 10 ng/g IMD attenuates escape manoeuvers while 100 ng/g IMD prevents the ability to fly and walk. We show that, at ecologically-relevant doses, IMD causes significant and lasting impairment of an important pathway involved with visual sensory coding and escape behaviours. These results show, for the first time, that a neonicotinoid pesticide directly impairs an important, taxonomically conserved, motion-sensitive visual network.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.313
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.306
Teacher spread0.283 · 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

Citations27
Published2017
Admission routes2
Has abstractyes

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