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Record W4376615345 · doi:10.3389/fmars.2023.1111466

Impact of vessel noise on feeding behavior and growth of zooplanktonic species

2023· article· en· W4376615345 on OpenAlexafffund
Ariane Aspirault, Gesche Winkler, Aurélie Jolivet, Céline Audet, Laurent Chauvaud, Francis Juanes, Frédéric Olivier, Réjean Tremblay

Bibliographic record

VenueFrontiers in Marine Science · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of VictoriaUniversité du Québec à Rimouski
FundersFonds de recherche du Québec – Nature et technologiesFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsBrachionusPlanktonMetamorphosisBiologyBlue musselZooplanktonRotiferPomacentridaeMytilusFisheryEcologyCoral reefOceanographyLarvaCoral reef fish

Abstract

fetched live from OpenAlex

Anthropogenic noise is a pervasive feature of the coastal ocean soundscape and is intensifying as vessel traffic activity increases. Low-frequency sounds from wave action on coastal reefs or anthropogenic noise have been shown to initiate larval settlement of marine invertebrates and accelerate metamorphosis to juvenile stages. These results suggest that some planktonic species can perceive and be impacted by anthropogenic sound. Hence, we tested the hypothesis that vessel noise has an impact on the feeding behavior of blue mussel ( Mytilus edulis ) veligers and of the copepod Eurytemora herdmani as well as on the growth of the rotifer Brachionus plicatilis. The results show that microalgae and feeding behavior of early life stages of mussels and copepods are not influenced by the presence of vessel noise. The growth of the rotifers was similar between the two sound treatments, but rotifers’ egg production in the absence of vessel noise was higher and eggs were also larger. Our results suggest that the effects of noise on plankton are complex; much more work is needed to unravel these often subtle effects.

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)0.000
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 score0.015
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.015
GPT teacher head0.256
Teacher spread0.240 · 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

Citations13
Published2023
Admission routes2
Has abstractyes

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