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Record W4378227764 · doi:10.3390/jmse11061115

Behavioural Responses to Ultrasound Antifouling Systems by Adult Solitary Ascidians

2023· article· en· W4378227764 on OpenAlexaff
Roberta Varello, Davide Asnicar, Jacopo Boaga, Francesca Cima

Bibliographic record

VenueJournal of Marine Science and Engineering · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Ecology and Invasive Species
Canadian institutionsHuntsman Marine Science Centre
FundersUniversità degli Studi di PadovaMinistero dell’Istruzione, dell’Università e della Ricerca
KeywordsCiona intestinalisBiofoulingBiologySiphon (mollusc)ZoologyPredationCionaEcologyFishery

Abstract

fetched live from OpenAlex

Ultrasonic antifouling devices are installed globally on a variety of vessel types and are marketed as an environmentally friendly method for biofouling control. The aim of this study was to examine the effects of ultrasound on adults of three species of common solitary ascidians (Ciona intestinalis, Ascidiella aspersa and Styela plicata). After a brief (10 s) exposure to two ultrasound frequencies (30 and 35 kHz), alterations in the frequency of siphon closing events and the length of time the siphons remained closed/open were observed. The results revealed that ascidians are able to perceive ultrasound, showing frequency-dependent behavioural responses that vary depending on the species and size of individuals involving both tactile receptors and an acoustic system homologous to the vertebrate inner ear. Continuous (5 h) 30 kHz exposure caused other types of responses, the most interesting of which was the long-term opening of the oral siphon, indicating a lack of reactivity to mechanical stimuli. This effect suggests a stress condition that could lead to increased vulnerability to predators and filter-feeding impairment. Therefore, knowledge of the acoustic sensitivity of sessile marine species appears to be essential for better understanding the potential effects of noise pollution on marine ecosystems.

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)0.001
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 score0.218
Threshold uncertainty score0.349

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.010
GPT teacher head0.215
Teacher spread0.205 · 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

Citations9
Published2023
Admission routes1
Has abstractyes

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