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Record W2613792764 · doi:10.7557/2.37.1.4115

Spatial and temporal changes in seasonal range attributes in a declining barren-ground caribou herd

2017· article· en· W2613792764 on OpenAlexaff
John A. Virgl, W. James Rettie, Daniel W. Coulton

Bibliographic record

VenueRangifer · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsRed River CollegeGolder Associates (Canada)
Fundersnot available
KeywordsRange (aeronautics)GeographyPopulationAbundance (ecology)Arboreal locomotionEcologyClimate changeUngulatePhysical geographyHerdEnvironmental scienceHabitatBiologyDemography

Abstract

fetched live from OpenAlex

From 1996 to 2015 the Bathurst caribou herd has declined from approximately 349,000 to 20,000 animals. Aboriginal traditional knowledge (TK) has recently observed the later arrival of the herd below the treeline, an attribute of the autumn range. Science also predicts that seasonal range attributes (e.g., area, location) likely vary with population size, and perhaps climate. We used Aboriginal TK and science to identify several seasonal range attributes that were ex­amined for changes through time (decreasing population abundance). Attributes of seasonal ranges for female Bathurst caribou were calculated using satellite radio-collar data from January 1996 through October 2013. Climate data from CircumArctic Rangifer Monitoring and Assessment Network were analyzed for trends from 1979 to 2009. Analyses showed a significant decrease in the area of post-calving and autumn ranges, but no changes in winter and spring ranges. Results supported Aboriginal TK that female caribou have shifted the autumn range farther from the treeline and moved into the forest later in the year. Analysis of climate variables found no trends at the spatio-temporal scale of the post-calving to autumn ranges. Working hypotheses to explain these patterns, which are not mutually exclusive, include reduced predation risk, increased use of core areas at lower population density, and greater utilization of areas of taiga where arboreal and ground lichen availability and accessibility are relatively higher than in the forest. This analysis demonstrates how including Aboriginal TK can lead to stronger connections and results, with potential to provide new and different insights for further investigations.

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 score0.125
Threshold uncertainty score0.929

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 head0.026
GPT teacher head0.257
Teacher spread0.231 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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