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Record W4239253405 · doi:10.1121/1.5101600

Temporal and spatial dependence of a yearlong record of sound propagation from the Canada Basin to the Chukchi Shelf

2019· article· en· W4239253405 on OpenAlexaffabout
Megan S. Ballard, Jason D. Sagers, Mohsen Badiey, John A. Colosi, Altan Turgut, Sean Pecknold, Ying-Tsong Lin, Andrey Proshutinsky, Richard Krishfield, Peter F. Worcester, Matthew A. Dzieciuch

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsGeologyCanyonContinental shelfStructural basinOceanographyCurrent (fluid)Spatial variabilityClimatologyGeomorphology

Abstract

fetched live from OpenAlex

During the Canada Basin Acoustic Propagation Experiment (CANAPE), low-frequency signals from five tomographic sources located in the Canada Basin were recorded by an array of hydrophones located on the Chukchi Shelf. The propagation distances ranged from 240 km to 520 km, and the propagation conditions changed from persistently ducted in the basin to seasonally upward refracting on the continental shelf. An analysis of the received level from the tomography sources revealed a spatial dependence in the onset of the seasonal increase in transmission loss, which was correlated with the locations of the sources in the basin. This observation led to the hypothesis that the water advected from Barrow Canyon westward over the continental slope by the Chukchi slope current contributes to the temporal and spatial dependence observed in the acoustic record. The water column properties and ice draft were measured by oceanographic sensors on the basin tomography moorings and by six arrays of oceanographic moorings on the continental shelf to characterize the temporal and spatial variability of the environment. This talk examines the range-dependent measurements and explains the observed variability in the received signals through propagation modeling. [Work sponsored by ONR.]

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.407
Threshold uncertainty score0.941

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.013
GPT teacher head0.226
Teacher spread0.213 · 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

Citations2
Published2019
Admission routes2
Has abstractyes

Explore more

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