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Record W4407859052 · doi:10.1038/s42003-025-07643-6

Road noise exposure over development increases baseline auditory activity and decision-making time in adult crickets

2025· article· en· W4407859052 on OpenAlexafffund
Erik A. Etzler, Hannah M. ter Hofstede, Darryl Gwynne, John M. Ratcliffe

Bibliographic record

VenueCommunications Biology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsWycliffe CollegeUniversity of WindsorUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsGovernment of Canada
KeywordsBaseline (sea)Noise exposureNoise (video)AudiologyEnvironmental scienceMedicineBiologyComputer scienceFisheryArtificial intelligenceHearing loss

Abstract

fetched live from OpenAlex

Female crickets reared in traffic noise have been reported to be faster or slower to locate male song than those reared in silence across species. We reared female Teleogryllus oceanicus in traffic noise and silence, and had adult females locate male song broadcast amidst traffic noise or silence. We recorded activity of two auditory interneurons in a subset of individuals under identical acoustic conditions. Regardless of rearing treatment, crickets were slower to leave their shelter when presented with male song in silence than in traffic noise, while crickets reared in traffic noise were also slower to leave overall. Crickets reared in traffic noise also had higher baseline AN2 activity, but rearing condition did not affect hearing thresholds or auditory response to male song. Our results demonstrate behavioural and auditory effects of long-term exposure to anthropogenic noise. Further, they support the idea that silence itself is a potentially aversive acoustic condition. A neuroethological approach reveals the impact of traffic noise on female crickets’ response to male song, demonstrating that developmental exposure increases adult cricket’s resting auditory activity and leads to protracted decision-making.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.822
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.016
GPT teacher head0.328
Teacher spread0.312 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

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