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Record W6910974336 · doi:10.5061/dryad.tb2rbp09r

Data from: Anthropogenic noise exposure over development increases baseline auditory activity and decision-making time in adult crickets

2024· dataset· en· W6910974336 on OpenAlexaff

Bibliographic record

VenueDRYAD · 2024
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTraffic noiseNoise (video)Affect (linguistics)SilenceNoise exposureBaseline (sea)Background noise

Abstract

fetched live from OpenAlex

Anthropogenic noise negatively affects some animals more so than others and often in different ways. Female crickets reared in traffic noise are reported to be faster or slower to locate male song than those reared in silence depending on species. No study has considered whether observed differences were due to hearing or decision-making. We reared female Teleogryllus oceanicus in traffic noise and silence, and adult females located 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.

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)0.004
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.038
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0380.032

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 head0.021
GPT teacher head0.315
Teacher spread0.294 · 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
GenreDataset

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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