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Underwater sound levels in the Canadian Arctic, 2014–2019

2021· article· en· W3157903749 on OpenAlexafffundabout
William D. Halliday, David R. Barclay, Amanda N. Barkley, Emmanuelle Cook, Jackie Dawson, Casey Hilliard, Nigel E. Hussey, Joshua M. Jones, Francis Juanes, Marianne Marcoux, Andrea Niemi, Shannon Nudds, Matthew K. Pine, Clark Richards, Kevin Scharffenberg, Kristin H. Westdal, Stephen J. Insley

Bibliographic record

VenueMarine Pollution Bulletin · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsFisheries and Oceans CanadaUniversity of OttawaUniversity of WindsorBedford Institute of OceanographyUniversity of VictoriaDalhousie UniversityWildlife Conservation Society Canada
FundersW. Garfield Weston FoundationGarfield Weston Foundation
KeywordsUnderwaterArcticEnvironmental scienceSound (geography)Baseline (sea)Sound pressureOceanographySea iceNoise (video)Arctic ice packSound exposureWind speedAmbient noise levelClimate changePhysical geographyGeographyGeologyAcousticsComputer science

Abstract

fetched live from OpenAlex

The Arctic has been a refuge from anthropogenic underwater noise; however, climate change has caused summer sea ice to diminish, allowing for unprecedented access and the potential for increased underwater noise. Baseline underwater sound levels must be quantified to monitor future changes and manage underwater noise in the Arctic. We analyzed 39 passive acoustic datasets collected throughout the Canadian Arctic from 2014 to 2019 using statistical models to examine spatial and temporal trends in daily mean sound pressure levels (SPL) and quantify environmental and anthropogenic drivers of SPL. SPL (50-1000 Hz) ranged from 70 to 127 dB re 1 μPa (median = 91 dB). SPL increased as wind speed increased, but decreased as both ice concentration and air temperature increased, and SPL increased as the number of ships per day increased. This study provides a baseline for underwater sound levels in the Canadian Arctic and fills many geographic gaps on published underwater sound levels.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.028
Threshold uncertainty score0.201

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.008
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
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 head0.021
GPT teacher head0.228
Teacher spread0.207 · 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

Citations28
Published2021
Admission routes3
Has abstractyes

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