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Record W4412871758 · doi:10.1121/10.0037362

Detection of the Kauai Beacon signal on Ocean Networks Canada’s hydrophones in the NE Pacific

2025· article· en· W4412871758 on OpenAlexaffabout
Lanfranco Muzi, David R. Barclay, David Huges

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsDalhousie UniversityOcean Networks Canada SocietyUniversity of Victoria
Fundersnot available
KeywordsPacific oceanOceanographySIGNAL (programming language)GeographyEnvironmental scienceTelecommunicationsGeologyComputer science

Abstract

fetched live from OpenAlex

We present a study of the Kauai Beacon signal as received by the hydrophones of Ocean Networks Canada’s (a University of Victoria Initiative) NorthEast Pacific Time-series Undersea Networked Experiments (NEPTUNE) observatory. Long range acoustic-propagation studies are used to produce basin-scale estimates of the variability of bulk ocean temperature and transmission loss. The Kauai Beacon source, located off of the north shore of the Hawaiian Island of Kauai, was designed to support such studies and has resumed its transmissions on a regular schedule since 2023. At a distance of approximately 4100 km from the Kauai Beacon and a depth close to the deep sound channel axis, the NEPTUNE stations around the Barkley Canyon have an unobstructed path to the source. Though Ocean Networks Canada has had a four-element volumetric array of Ocean Sonics icListen HF hydrophones at the “Barkley Node” site since 2021, a single low-frequency icListen AF hydrophone was deployed in the summer of 2024 at the “Barkley Upper Slope” site, specifically for the purpose of supporting the reception of the Kauai Beacon signal.

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)0.001
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 score0.899
Threshold uncertainty score0.204

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.216
Teacher spread0.208 · 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

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicUnderwater Acoustics ResearchFrench-language works237,207