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Record W4220929203 · doi:10.1139/as-2021-0032

Photographic evidence of tagging impacts for two beluga whales from the Cumberland Sound and western Hudson Bay populations

2022· article· en· W4220929203 on OpenAlexafffundvenue
Kasey P. Ryan, Stephen D. Petersen, Steven H. Ferguson, C-Jae C. Breiter, Cortney A. Watt

Bibliographic record

VenueArctic Science · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsFisheries and Oceans CanadaUniversity of Manitoba
FundersNunavut Wildlife Management BoardChurchill Northern Studies CentreRoyal Bank of CanadaWorld Wildlife Fund
KeywordsBeluga WhaleBelugaSound (geography)BayAerial surveyFisheryGeographyArcticPopulationWhaleOceanographyCetaceaMarine mammalArchaeologyRemote sensingBiologyGeology

Abstract

fetched live from OpenAlex

Beluga whale ( Delphinapterus leucas (Pallas, 1776)) movements, habitat use, and diving behaviour have been studied using satellite-linked transmitters for decades. The inaccessibility of Arctic and subarctic habitats makes these instruments especially valuable for beluga research. The long-term effects that tags and tag attachments have on belugas, however, are not well known because resightings occur relatively infrequently. Here, we describe two belugas photographed during photographic monitoring programs of two populations: western Hudson Bay and Cumberland Sound. The beluga photographed in western Hudson Bay had scars consistent with the tag pins migrating out, which is thought to occur when the tag is pulled posteriorly due to drag. The beluga photographed in Cumberland Sound had all three tag pins still in place 11–21 years after they were inserted. Both whales appeared to be in good body condition with no evidence of infection, and the beluga from Cumberland Sound was accompanied by a 1-year-old calf. Resightings of previously tagged whales are infrequent for the western Hudson Bay population and have never been documented in Cumberland Sound. However, through long-term photographic monitoring programs, additional sightings may provide more information regarding the method of tag loss and the long-term effects of tagging on whale health and productivity.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.003
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.068
GPT teacher head0.324
Teacher spread0.256 · 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

Citations7
Published2022
Admission routes3
Has abstractyes

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