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Record W4223429222 · doi:10.1121/10.0010208

Marine mammal detections on the Chukchi Plateau 2009–2020

2022· article· en· W4223429222 on OpenAlexaff
Kathleen M. Stafford, Humfrey Melling, Sue E. Moore, Catherine L. Berchok, Eric K. Braen, Arial Brewer, Brynn M. Kimber

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsFisheries and Oceans Canada
FundersNational Marine Fisheries ServiceOffice of ScienceNational Oceanic and Atmospheric Administration
KeywordsSubarctic climateBeluga WhaleArcticMarine mammalPlateau (mathematics)BelugaSound (geography)OceanographyWhaleEnvironmental scienceFisheryGeographyGeologyBiology

Abstract

fetched live from OpenAlex

The Arctic Ice Monitoring (AIM) observatory has been maintained on the Chukchi Plateau at 75.1° N 168.0° W nearly continuously since 2003. The AIM site consists of a submerged mooring that, since October 2008, has been instrumented with a passive acoustic recorder to sample ambient sound, with a focus on marine mammal detections in the High Arctic. Year-long data sets for 2009, 2012, and 2014-2020 were analyzed for the presence of signals from Arctic species including bowhead and beluga whales, bearded seals, and walrus. Calls from subarctic ribbon seals were commonly detected in autumn months, suggesting they have expanded their distribution much further northward. Killer whale calls were detected in recent years providing evidence that they have moved further north into the Pacific Arctic. No other subarctic cetaceans were heard. Year-round passive acoustic sampling of sounds produced by marine mammals over a decadal timescale has enhanced our understanding of how climate-driven changes in biodiversity are affecting even the very High Arctic.

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 score0.266
Threshold uncertainty score0.529

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.011
GPT teacher head0.221
Teacher spread0.209 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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