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Record W6892312280 · doi:10.5061/dryad.g1hk2

Data from: Noise affects resource assessment in an invertebrate

2017· dataset· en· W6892312280 on OpenAlexaff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2017
Typedataset
Languageen
Field
Topic
Canadian institutionsQueen's University
Fundersnot available
KeywordsHermit crabNoise (video)Resource (disambiguation)Selection (genetic algorithm)InvertebrateNoise pollutionAffect (linguistics)Traffic noise

Abstract

fetched live from OpenAlex

Anthropogenic noise is a global pollutant, affecting animals across taxa. However, how noise pollution affects resource acquisition is unknown. Hermit crabs (Pagurus bernhardus) engage in detailed assessment and decision-making when selecting a critical resource, their shell; this is crucial as individuals in poor shells suffer lower reproductive success and higher mortality. We experimentally exposed hermit crabs to anthropogenic noise during shell selection. When exposed to noise, crabs approached the shell faster, spent less time investigating it, and entered it faster. Our results demonstrate that changes in the acoustic environment affect the behaviour of hermit crabs by modifying the selection process of a vital resource. This is all the more remarkable given that the known cues used in shell selection involve chemical, visual and tactile sensory channels. Thus, our study provides rare evidence for a cross-modal impact of noise pollution.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Open science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.440
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0020.004
Open science0.0260.025
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.001

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 head0.060
GPT teacher head0.348
Teacher spread0.288 · 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; both teacher heads agree on what is shown here.

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
Published2017
Admission routes1
Has abstractyes

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