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Record W3120349920 · doi:10.1139/as-2020-0034

Cortisol levels in narwhal (<i>Monodon monoceros</i>) blubber from 2000 to 2019

2021· article· en· W3120349920 on OpenAlexaffvenue
Cortney A. Watt, James Simonee, Vincent L’Hérault, Ruokun Zhou, Steven H. Ferguson, Marianne Marcoux, Sandra R. Black

Bibliographic record

VenueArctic Science · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of CalgaryARCTIConnexionFisheries and Oceans Canada
Fundersnot available
KeywordsBlubberPopulationBiologyFisheryMedicine

Abstract

fetched live from OpenAlex

Narwhals (Monodon monoceros Linnaeus, 1758) summering on northern Baffin Island are experiencing increases in vessel traffic related to an iron-ore mine operated by Baffinland Iron Mines Corporation; how this increase in vessel traffic may impact narwhal is currently unknown. Cortisol is a stress response hormone and a stress indicator in marine mammals. This study evaluated cortisol levels in narwhal blubber sampled during subsistence harvests prior to project-related vessel traffic (2000–2006), during project-related vessel traffic (2013–2019), and during a high-stress entrapment event that occurred in 2015. There was a significant increase in cortisol levels from pre- (0.81 ± 0.45 ng/g (±SE)) to during (1.81 ± 0.48 ng/g (±SE)) project-related vessel traffic (over 100% higher), and both were significantly lower than cortisol levels from animals sampled during an entrapment event (10.52 ± 0.59 ng/g (±SE)). Increased vessel traffic, changing ice conditions, altered Arctic food webs, increased predation pressure from killer whales, and cumulative impacts from these sources likely all contribute to increased stress levels for narwhals. Thus, there is a need for continued monitoring of stress responses (i.e., cortisol levels) and other health indicators in narwhals to understand how individual fitness and the population will be impacted over time.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.003

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 head0.023
GPT teacher head0.259
Teacher spread0.236 · 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 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

Citations13
Published2021
Admission routes2
Has abstractyes

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