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Record W2801916394 · doi:10.1121/1.5036009

Underwater noise measurements during a year long Shallow Water Canada Basin Acoustic Propagation experiment from 2016 to 2017

2018· article· en· W2801916394 on OpenAlexaffabout
Mohsen Badiey, Ying-Tsong Lin, Sean Pecknold, Altan Turgut, Megan S. Ballard, Jason D. Sagers, Christopher Whitt

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsUnderwaterNoise (video)Ambient noise levelGeologyStructural basinWaves and shallow waterOceanographyAcousticsLine (geometry)Environmental scienceSound (geography)PhysicsGeomorphology

Abstract

fetched live from OpenAlex

A year-long, multi-institution, acoustical oceanographic measurement on the Chukchi Continental Shelf region of the Canada Basin started on October 2016 is reported. Ten vertical receiver line arrays and a horizontal receiver line array were deployed together with oceanographic sensors to measure the sound speed profiles, ice formation, and currents over an area of approximately 30 km by 50 km. Various aspects of the noise including spectral fluctuations, directionality, seasonal dependence, and intensity fluctuations are studied over time. In this paper, we present preliminary measurement results depicting spatial and temporal distribution of underwater background noise at the experiment site. [Work supported by ONR321 OA.]

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 score0.356
Threshold uncertainty score0.716

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)0.0000.000

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 head0.027
GPT teacher head0.250
Teacher spread0.223 · 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
Published2018
Admission routes2
Has abstractyes

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