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Record W4221110872 · doi:10.3390/ani12060672

The Adrenal Cortisol Response to Increasing Ambient Temperature in Polar Bears (Ursus maritimus)

2022· article· en· W4221110872 on OpenAlexafffund
Emily M. Leishman, Maria Franke, Jill Marvin, Dylan McCart, Carol R. Bradford, Zoltan S. Gyimesi, Anne F. Nichols, Marie-Pierre Lessard, David Page, C-Jae Breiter, Laura H. Graham

Bibliographic record

VenueAnimals · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsChurchill Northern Studies CentreSépaqToronto ZooUniversity of Guelph
FundersUniversity of Guelph
KeywordsUrsus maritimusPolarSeasonal breederJuvenileAnimal scienceHumidityThermoregulationBiologyEcologyGeographyMeteorology

Abstract

fetched live from OpenAlex

Our objective was to identify the upper ambient temperature threshold that triggers an increase in cortisol in response to increased thermoregulatory demands in polar bears. The results reported here include endocrine data collected over two years from 25 polar bears housed in 11 accredited zoological institutions across North America. The effects of ambient temperature, sex, age group (juvenile, adult, elderly), breeding season and humidity on fecal cortisol metabolite (FCM) concentrations (N = 8439 samples) were evaluated using linear mixed models. Ambient temperatures were placed into five different categories: <5 °C, 6−10 °C, 11−15 °C, 16−20 °C, and >20 °C. Ambient temperature and humidity had a significant (p < 0.05) effect on FCM concentrations with significant (p < 0.05) interactions of sex, age and breeding season. Once biotic factors were accounted for, there was a significant (p < 0.05) increase in FCM concentrations associated with ambient temperatures above 20 °C in adult polar bears. The implications of these findings for the management of both zoo and wild polar bears are discussed.

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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 science0.0000.000
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 head0.010
GPT teacher head0.238
Teacher spread0.228 · 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

Citations10
Published2022
Admission routes2
Has abstractyes

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