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Record W7160966519 · doi:10.1121/10.0041043

Biological contributions to the soundscape at the Main Endeavour and Lucky Strike hydrothermal vent fields

2025· article· en· W7160966519 on OpenAlexaffabout
Brendan Smith, David R. Barclay

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsDalhousie University
Fundersnot available
KeywordsHydrothermal ventSoundscapeContext (archaeology)Seafloor spreadingHydrothermal circulationSound (geography)HydrophoneSubmarine volcano

Abstract

fetched live from OpenAlex

Deep sea hydrothermal vents host unique marine life adapted to survive in the high-temperature, caustic environment created by the geothermally heated vent fluid expelled from the seafloor. Some of these organisms produce sound, and it has been hypothesized that some may also use sound produced by hydrothermal vents as a cue for habitat settlement. Thus, an understanding of the acoustic environment at hydrothermal vent fields and the biological contribution to the soundscape is a critical component of an effective environmental monitoring strategy. Hydrophone measurements at the Main Endeavour vent field (MEF, from Ocean Networks Canada’s NEPTUNE observatory) and the Lucky Strike hydrothermal vent field (from the European Multidisciplinary Seafloor and water column Observatory) were analyzed to detect and quantify transient acoustic signals, which may be of biological origin. Potential source mechanisms are hypothesized, and comparisons are made between contemporaneous video and audio recordings at MEF to aid in source identification. These biological signals are also presented in the context of the broader soundscape at both sites. Implications to the establishment of baseline acoustic conditions at hydrothermal vent fields and environmental monitoring are discussed.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.061
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.255
Teacher spread0.245 · 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 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

Citations0
Published2025
Admission routes2
Has abstractyes

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