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Record W2886066278 · doi:10.1898/nwn17-20.1

Mule Deer, White-Tailed Deer, and Wolves in Jasper National Park, Alberta: 35 Years of Sightings, 1981–2016

2018· article· en· W2886066278 on OpenAlexafffundabout
Dick Dekker, Mark C. Drever

Bibliographic record

VenueNorthwestern Naturalist · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsEnvironment and Climate Change Canada
FundersParks Canada
KeywordsOdocoileusCanisNational parkGeographyPredationPopulationWildlifePopulation declineHabitatGray wolfEcologyForestryBiologyDemographyArchaeology

Abstract

fetched live from OpenAlex

On the basis of annual observations collected over 35 y, we chronicled the trends in abundance of Mule Deer (Odocoileus hemionus) and White-tailed Deer (Odocoileus virginianus) in semi-open montane habitat in the Devona district of Jasper National Park (JNP), Alberta, 1981–2016. During 722 d of observations conducted in winter over this period, we recorded a decline in Mule Deer and the incursion of White-tailed Deer into the park. Of a total of 429 deer sighted, White-tailed Deer increased from an average of 0.08 sightings/d to 0.73/d, whereas the native Mule Deer declined from 0.42 sightings/d to 0.01/d. Over the same time span, sightings of all deer increased from 0.51/d to 0.74/d. Although the ultimate cause of the opposing population trends of the 2 deer species is not certain, we review the proximate causes discussed in relevant literature, and we compare the results of our census to a list of deer killed by vehicle collisions on JNP roads and highways. As a measure of the presence of Gray Wolves (Canis lupus) in the study area, we recorded the largest size of wolf packs sighted each year and found no trend over time. The question of whether wolf predation on the 2 deer species could account for their opposing population trends remains to be investigated.

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 score0.069
Threshold uncertainty score0.946

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 head0.008
GPT teacher head0.223
Teacher spread0.215 · 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

Citations2
Published2018
Admission routes3
Has abstractyes

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