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Record W2331940702 · doi:10.1139/z10-115

Habitat selection and spatial relationships of black bears (<i>Ursus americanus</i>) with woodland caribou (<i>Rangifer tarandus caribou</i>) in northeastern Alberta

2011· article· en· W2331940702 on OpenAlexaffvenueabout
A. David M. Latham, M. Cecilia Latham, Mark S. Boyce

Bibliographic record

VenueCanadian Journal of Zoology · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWoodland caribouUrsusPredationEcologyBogHabitatBlack spruceBiologyBorealWoodlandTaigaRange (aeronautics)GeographyPeatPopulationDemography

Abstract

fetched live from OpenAlex

Populations of woodland caribou ( Rangifer tarandus caribou (Gmelin, 1788)) have declined across much of their range. Wolves ( Canis lupus L., 1758) are believed to be responsible for the majority of mortality in adult female caribou; however, we hypothesize that other predators such as black bears ( Ursus americanus Pallas, 1780) may be important contributors to calf mortality. We assessed habitat selection by black bears and spatial relationships of caribou – black bears during the caribou calving season in northeastern Alberta, Canada. Black bears avoided bogs and fens, while selecting upland mixed woods and various industrial features. Conversely, caribou showed strong selection for bogs and fens relative to bears, supporting the hypothesis that caribou in the boreal forest attempt to minimize predation risk by selecting peatlands to avoid areas frequented by predators. However, habitat selection by individual black bears was highly variable and some bears selected habitats similar to those selected by caribou, i.e., bogs and fens. Bears that specialize on foraging in peatlands might be responsible for some of the predation on caribou calves. Because declines in caribou populations have resulted from a combination of high adult female and calf mortalities, management actions to conserve woodland caribou should consider the entire suite of potential predators rather than focusing only on wolves.

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 score0.648
Threshold uncertainty score0.670

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 head0.013
GPT teacher head0.180
Teacher spread0.167 · 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

Citations124
Published2011
Admission routes3
Has abstractyes

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