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Record W3162367442 · doi:10.7939/r3-4s5r-jp80

A burning question: The spatial response of woodland caribou to wildfire in northeastern Alberta

2020· article· en· W3162367442 on OpenAlexaboutno aff
Sean M. Konkolics

Bibliographic record

VenueUniversity of Alberta Library · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsWoodland caribouWoodlandGeographyForestryEcologyHabitat

Abstract

fetched live from OpenAlex

The Canadian Federal Recovery Strategy for woodland caribou classifies areas burned by wildfire in the last 40 years as disturbed habitat for woodland caribou. This delineation of fire disturbance has major economic and social implications across Canada. Caribou have been shown to avoid burned areas, but our understanding of the implications of burned habitats on survival is unclear. Previously, studies used coarse mapping techniques that failed to delineate unburned residual patches within the burn complex, which have recently been proposed to provide undisturbed habitat for caribou. To assess the importance of burns and unburned residual patches, we examined the multi-scale resource selection of these two landcovers and the implications of using burns to adult survival of caribou for 201 individuals dispersed among six Alberta caribou populations. We found that caribou avoided both the burn complex and unburned residual patches in all seasons. However, increased use of burned habitats did not influence survival, while use of uplands significantly decreased survival. Collectively, these results suggest that burns and the corresponding residual patches are indeed low-quality habitat for caribou; however, a negligible survival effect suggests the classification of burned habitat as disturbed may be overstated by current recovery strategy recommendations. This study provides important information for herd-level management decisions and defining critical habitat under the federal mandates.

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 score0.098
Threshold uncertainty score0.977

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)0.0010.000

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 head0.004
GPT teacher head0.162
Teacher spread0.157 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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