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Record W4406369262 · doi:10.1121/10.0034918

Passive acoustic monitoring of a major seismic event at the Main Endeavour Hydrothermal Vent Field

2024· article· en· W4406369262 on OpenAlexaffabout
Brendan Smith, David R. Barclay

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsDalhousie University
Fundersnot available
KeywordsHydrothermal ventHydrothermal circulationEvent (particle physics)SeismologyField (mathematics)GeologyEnvironmental science

Abstract

fetched live from OpenAlex

A major seismic event occurred in the region of the Main Endeavour Hydrothermal Vent Field (MEF) on March 5–6, 2024. During this 48-h period, a 4.1 magnitude event and up to 200 earthquakes per hour were recorded by Ocean Networks Canada’s (ONC) cabled North-East Pacific Time-series Undersea Networked Experiments (NEPTUNE) observatory. Low frequency waterborne acoustic signals associated with the earthquakes were detected by a four-element “box-type” hydrophone array located near multiple black smoker hydrothermal vents, with two of these elements being active during the event. Broadband signals up to 10 kHz were also measured by the hydrophones. Power spectral densities before, during, and after the seismic event show increases up to 50 dB relative to ambient levels below 100 Hz and increases up to 20 dB between 100 Hz and 10 kHz during the event. Power spectral densities between 100 Hz and 10 kHz remain elevated by approximately 5 dB more than one week following the seismic event. Power spectral density and complex coherence measurements, as well as cross-correlation with other sensors at MEF, suggest that these broadband signals may be associated with changes to the hydrothermal vent field resulting from the increased seismicity in this region.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.009
GPT teacher head0.235
Teacher spread0.226 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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