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Record W3035757568 · doi:10.1139/er-2019-0033

Underwater noise and Arctic marine mammals: review and policy recommendations

2020· article· en· W3035757568 on OpenAlexafffundvenue
William D. Halliday, Matthew K. Pine, Stephen J. Insley

Bibliographic record

VenueEnvironmental Reviews · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsWildlife Conservation Society Canada
FundersFisheries and Oceans CanadaTransport CanadaMitacsWildlife Conservation SocietyWorld Wildlife Fund
KeywordsArcticUnderwaterNoise (video)Environmental scienceSea iceAmbient noise levelArctic ice packThe arcticOceanographySound (geography)GeologyComputer science

Abstract

fetched live from OpenAlex

Underwater noise is an important issue globally. Underwater noise can cause auditory masking, behavioural disturbance, hearing damage, and even death for marine animals. While underwater noise levels have been increasing in nonpolar regions, noise levels are thought to be much lower in the Arctic where the presence of sea ice limits anthropogenic activities. However, climate change is causing sea ice to decrease, which is allowing for increased access for noisy anthropogenic activities. Underwater noise may have more severe impacts in the Arctic compared with nonpolar regions due to a combination of lower ambient sound levels and increased sensitivity of Arctic marine animals to underwater noise. Here, we review ambient sound levels in the Arctic, as well as the reactions of Arctic and sub-Arctic marine mammals to underwater noise. We then relate what is known about underwater noise in the Arctic to policies and management solutions for underwater noise and discuss whether Arctic-specific policies are necessary.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.847
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.002

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 head0.032
GPT teacher head0.269
Teacher spread0.237 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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

Citations62
Published2020
Admission routes3
Has abstractyes

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