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Record W3154539864 · doi:10.1515/mammalia-2020-0041

Long-term stress level in a small mammal species undergoing range expansion

2021· article· en· W3154539864 on OpenAlexafffundabout
Adrien André, Johan Michaux, Jorge Gaitan, Virginie Millien

Bibliographic record

VenueMammalia · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsMcGill University
FundersFonds pour la Formation à la Recherche dans l’Industrie et dans l’AgricultureNatural Sciences and Engineering Research Council of Canada
KeywordsPeromyscusMammalRange (aeronautics)EcologySpecies distributionGeographyBiologyHabitat

Abstract

fetched live from OpenAlex

Abstract Rapid climate change is currently altering species distribution ranges. Evaluating the long-term stress level in wild species undergoing range expansion may help better understanding how species cope with the changing environment. Here, we focused on the white-footed mouse ( Peromyscus leucopus ), a widespread small mammal species in North-America whose distribution range is rapidly shifting northward. We evaluated long-term stress level in several populations of P. leucopus in Quebec (Canada), from the northern edge of the species distribution to more core populations in Southern Quebec. We first tested the hypothesis that populations at the range margin are under higher stress than more established populations in the southern region of our study area. We then compared four measures of long-term stress level to evaluate the congruence between these commonly used methods. We did not detect any significant geographical trend in stress level across our study populations of P. leucopus . Most notably, we found no clear congruence between the four measures of stress level we used, and conclude that these four commonly used methods are not equivalent, thereby not comparable across studies.

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 score0.999

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)0.0930.001

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 head0.067
GPT teacher head0.257
Teacher spread0.190 · 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

Citations1
Published2021
Admission routes3
Has abstractyes

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