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Record W2150752844 · doi:10.1644/13-mamm-a-046.1

Assessing the impact of live-capture, confinement, and translocation on stress and fate in eastern gray squirrels

2013· article· en· W2150752844 on OpenAlexaff
Curtis O. Bosson, Rupert Palme, Rudy Boonstra

Bibliographic record

VenueJournal of Mammalogy · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsChromosomal translocationBiologyStressorFecesZoologyEcology

Abstract

fetched live from OpenAlex

Live-capture and translocation are methods to deal with nuisance eastern gray squirrels in North America, but it is unknown how these methods affect squirrel physiology or survival. In this study we validated an enzyme immunoassay (EIA) to measure fecal Cortisol metabolites (FCMs) in gray squirrels; assessed their stress response relative to the positioning of the live trap (sun, shade, or control); and assessed the impact of translocation on their long-term stress, movement patterns, and survival using FCM levels, body mass changes, and radiotelemetry. We found that a 5α-pregnane-3β,11β,21-triol-20-one EIA reliably detected acute stress in gray squirrel feces 12–24 h after the stressor; live traps positioned in the sun resulted in higher peak FCM levels compared with traps positioned in the shade; translocated squirrels experienced a 10% mortality rate, compared with no mortality in the controls, although overall fates were the same; translocated squirrels initially explored more and dispersed farther than controls, but after 2 weeks made similar movements; and after controlling for the effect of season, translocation did not affect long-term FCM levels or body mass; this conclusion must be tempered by the low number of recaptures. Our study demonstrates the utility of the FCM assay; that gray squirrels are extremely sensitive to capture, handling, and confinement; and that live-capture must be done in a way that minimizes exposure to additive environmental stressors.

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 score0.011
Threshold uncertainty score0.484

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 head0.308
Teacher spread0.283 · 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

Citations42
Published2013
Admission routes1
Has abstractyes

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