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Record W2768375454 · doi:10.1121/1.5014900

Acoustic propagation from the Canadian Basin to the Chukchi Shelf

2017· article· en· W2768375454 on OpenAlexaffabout
Megan S. Ballard, Jason D. Sagers, Ying-Tsong Lin, Mohsen Badiey, Peter F. Worcester, Sean Pecknold

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsCanada BasinGeologyBeaufort seaContinental shelfArcticOceanographyStructural basinBeaufort scaleThe arcticSound (geography)ClimatologyGeomorphology

Abstract

fetched live from OpenAlex

The Pacific Arctic Region, encompassing the Bering, Chukchi, Western Beaufort, and Eastern Siberian shelves and seas, has experienced decadal changes in atmospheric conditions, seasonal sea-ice coverage, and seawater temperature. In the summer of 2016, the Canada Basin Acoustic Propagation Experiment (CANAPE) was conducted to understand the changing soundscape and to explore the use of acoustic signals as a remote sensing tool in the modern Arctic. During the experiment, low-frequency signals from five tomographic sources located in the Canada Basin were recorded by a short vertical line array of hydrophones deployed from a research vessel. The recordings were made at seven stations located in the Canada Basin, on the continental rise, and on the Chukchi Shelf. The propagation distances ranged from 50 km to 500 km, and the propagation conditions changed from ducted by the Beaufort Lens in the basin to upward refracting on the continental shelf. Multiple measurements of the sound speed profile were acquired at each station to characterize the temporal and spatial variability of the sound speed field. 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.884
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0030.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.257
Teacher spread0.234 · 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.

Study designSimulation or modeling
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

Citations3
Published2017
Admission routes2
Has abstractyes

Explore more

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