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Record W7160941568 · doi:10.1121/10.0040477

Observations of scattered surface duct arrivals from acoustic transmissions in the Beaufort duct using a tomographic array during 2016–2017

2025· article· en· W7160941568 on OpenAlexaboutno aff
John Colosi, Matthew Dzieciuch, Peter F. Worcester, Bruce D. Cornuelle, Heriberto J. Vazquez

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsnot available
Fundersnot available
KeywordsDuct (anatomy)HydrophoneBroadbandTransmitterBeaufort scaleUnderwaterBeaufort seaUnderwater acousticsTransmission loss

Abstract

fetched live from OpenAlex

The 2016–2017 Canada Basin Acoustic Propagation Experiment (CANAPE) was conducted to assess the effects of the changing Beaufort Gyre on low-frequency underwater acoustic propagation and ambient sound. A 150-km radius ocean acoustic tomography array was deployed with six transceivers and a distributed vertical line array (DVLA) measuring the impulse responses every 4 h with broadband signals centered from 172.5 to 275 Hz. The nominal transceiver source depth was 175-m, placing them near the Beaufort duct axis, and the 60 hydrophone DVLA spanned 50 to 600 m. The Beaufort Duct (BD; approximately 90 to 240-m depth) and the surface Duct (SD; approximately 0 to 90-m depth) form a coupled double-duct system. Previous work has described the statistics of the BD arrivals [J.Acoust. Soc. Am. 158, 38–50] so this analysis focuses on the SD. Because of lossy ice cover, the SD arrivals are only observed during open water and when the ice thickness does not exceed roughly 0.5 m. SD energy is detectable as an arrival coming in after the BD arrival but only on the shallowest hydrophones. The SD arrival is seen to be highly variable in both space and time. Mechanisms responsible for this variability will be evaluated.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.277
Threshold uncertainty score0.551

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.043
GPT teacher head0.286
Teacher spread0.242 · 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 routes1
Has abstractyes

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