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Record W4243473468 · doi:10.24124/2012/bpgub874

Impacts of industrial developments on the distribution and movement ecology of wolves (Canis lupus) and woodland caribou (Rangifer tarandus caribou) in the South Peace region of British Columbia.

2012· dissertation· en· W4243473468 on OpenAlexafffundabout
Elizabeth Parr Williamson-Ehlers

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsCanadian HeritageUniversity of Northern British ColumbiaLibrary and Archives Canada
FundersHabitat Conservation Trust FoundationMinistry of Forests, Lands and Natural Resource OperationsCanadian Association of Petroleum ProducersUniversity of Northern British Columbia
KeywordsWoodland caribouEcologyCanisGeographyTaigaHabitatPredationBorealDisturbance (geology)WoodlandPredatorBiology

Abstract

fetched live from OpenAlex

Habitat alterations from anthropogenic disturbances across northeastern British Columbia have resulted in large-scale modifications to predator-prey dynamics. I used GPS collar locations and field data to quantify the responses of wolves (Canis lupus) and woodland caribou (Rangifer tarandus caribou) to the cumulative effects of industrial disturbance. I developed seasonal resource selection functions for caribou and count models of habitat occupancy for wolves. I also related wolf movements to caribou habitat and industrial features. Caribou occupying the boreal forest likely are more at risk from industrial developments. My results suggest that caribou occupying these ecosystems are subject to disturbance by human activity and a greater risk of spatial interactions with wolves. However, these relationships are complicated by the positive and negative responses of wolves to landscape change and the distribution of other prey and predator species. --P. i.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.056
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.206
Teacher spread0.191 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2012
Admission routes3
Has abstractyes

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