MétaCan
Menu
Back to cohort
Record W4362669373 · doi:10.3390/jzbg4020026

Monitoring Thermoregulation Patterns in Asian Elephants (Elephas maximus) in Winter Months in Southwestern Ontario Using Infrared Thermography

2023· article· en· W4362669373 on OpenAlexaffabout
Janel Lefebvre, Charlie Gray, Taryn Prosser, Amy A. Chabot

Bibliographic record

VenueJournal of Zoological and Botanical Gardens · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEffects of Environmental Stressors on Livestock
Canadian institutionsQueen's University
Fundersnot available
KeywordsElephasAsian elephantThermographyThermoregulationPopulationBiologyEcologyInfraredMedicine

Abstract

fetched live from OpenAlex

Given the current and future threats to Asian elephants (Elephas maximus), maintaining a sustainable ex situ population is crucial for the longevity of the species. Using Infrared Thermography (IRT), thermoregulation of Asian elephants at low ambient temperatures was examined. Thermal images were taken at 15 min intervals over 60–90-min observation periods, once weekly, during January and February 2022. A total of 374 images were examined from 10 Asian elephants, which varied from 1 to 56 years of age. Data from thermograms of the ear and body were interpreted in view of weight, age and behavior. Variability in surface temperature was found most frequently in the ears, occasionally presenting as thermal windows—areas with dense underlying blood supply that aid in heat exchange. Thermal windows occurred most frequently in the distal, then medial, regions of the ear. The pattern of appearance of thermal windows in the ear provides support that the increase of blood flow is utilized as a method of warming. This preliminary study provides key insight into Asian elephant thermoregulation, suggesting that the species may be more well-adapted to lower ambient temperatures than previously thought.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.025
GPT teacher head0.235
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 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

Citations2
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Zoological and Botanical GardensSame topicEffects of Environmental Stressors on LivestockFrench-language works237,207