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Record W3082019803 · doi:10.1111/mms.12732

Vocalizations of bearded seals (<scp><i>Erignathus barbatus</i></scp>) and their influence on the soundscape of the western Canadian Arctic

2020· article· en· W3082019803 on OpenAlexafffundabout
Annika F. Heimrich, William D. Halliday, Héloïse Frouin‐Mouy, Matthew K. Pine, Francis Juanes, Stephen J. Insley

Bibliographic record

VenueMarine Mammal Science · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsWildlife Conservation Society CanadaUniversity of Victoria
FundersW. Garfield Weston FoundationMarine Environmental Observation Prediction and Response NetworkWorld Wildlife Fund
KeywordsSoundscapeArcticSound (geography)Marine mammalSea iceSeal (emblem)HarbourBlubberHabitatUnderwaterFisheryGeographyOceanographyBiologyEcologyGeologyArchaeology

Abstract

fetched live from OpenAlex

Abstract The soundscape is a crucial habitat feature for marine mammals. This study investigates the contribution of bearded seal vocalizations to the soundscape in the western Canadian Arctic, and also the vocal characteristics of bearded seals relative to sea ice conditions. Passive acoustic data were recorded near Sachs Harbour between August 2015 and July 2016. Sound pressure levels (SPL) in the 50–1,000 Hz and 1–10 kHz bands increased as the total duration of all bearded seal vocalizations increased, and this relationship was moderated by sea ice concentration. Bearded seals in this region had an overlapping vocal repertoire with bearded seals in other areas of the Arctic, and had seven additional vocalizations that have not been previously documented for this region. This study is the first detailed assessment of the influence of bearded seal calls on SPL, which shows the high potential of bearded seals to influence underwater sound levels during the mating season. Bearded seals live in a changing Arctic seascape, and their influence on the soundscape may shift as sea ice continues to diminish. It is imperative that acoustic monitoring continues within the Arctic, and this study provides a baseline for future monitoring as the Arctic continues to change.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.033
Threshold uncertainty score0.892

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.002
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 head0.200
Teacher spread0.187 · 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

Citations18
Published2020
Admission routes3
Has abstractyes

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