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Record W2163262582 · doi:10.7557/2.27.4.326

Managing fire for woodland caribou in Jasper and Banff National Parks

2007· article· en· W2163262582 on OpenAlexafffund
Landon Shepherd, Fiona K. A. Schmiegelow, S. Ellen Macdonald

Bibliographic record

VenueRangifer · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of AlbertaParks Canada
FundersUniversity of AlbertaParks CanadaAlberta Conservation Association
KeywordsWoodland caribouGeographyWoodlandHabitatLichenEcologyRange (aeronautics)Environmental scienceForestryBiology

Abstract

fetched live from OpenAlex

Woodland caribou (Rangifer tarandus caribou) populations in Jasper (JNP) and Banff National Parks (BNP) have declined since the 1970s, coincident with reduced fire activity in both parks, relative to historic levels. Some researchers have suggested that long periods without fire may cause habitat deterioration for woodland caribou, primarily by reducing available lichen forage. We examined winter habitat selection by woodland caribou at coarse and fine scales based on GPS-derived telemetry data and used models that included stand origin (decade), topography, and several stand structure variables that are related to time since fire, to explore relationships among caribou, lichen, and fire history. Based on the relationships illustrated by the models, we assessed how fire management could be applied to caribou conservation in JNP and BNP. At a coarse scale, caribou selected old forest (> 75 years) in landscapes that have likely experienced less frequent wildfire. While the abundance of Cladonia spp. influenced caribou use at fine scales, a preference for areas with older trees within stands was also significant. We conclude that short-term habitat protection for woodland caribou in JNP and BNP likely requires fire exclusion from caribou range.

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 score0.006
Threshold uncertainty score0.272

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 head0.009
GPT teacher head0.233
Teacher spread0.224 · 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

Citations9
Published2007
Admission routes2
Has abstractyes

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