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Record W2317478514 · doi:10.1080/14888386.2012.705110

Monitoring habitat condition changes during winter and pre-calving migration for Bathurst Caribou in northern Canada

2012· article· en· W2317478514 on OpenAlexaffabout
W. Chen, Donald E. Russell, Anne Gunn, Bruno Croft, Richard Fernandes, Huabiao Zhao, J. Li, Yu Zhang, Klaus Koehler, Ian Olthof, Robert Fraser, Sylvain G. Leblanc, G.R. Henry, Rosemary G. White, Greg Finstad

Bibliographic record

VenueBiodiversity · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of British Columbia HospitalCanadian Food Inspection AgencyYukon UniversityNatural Resources Canada
Fundersnot available
KeywordsTundraSnowIce calvingAbundance (ecology)HabitatPhysical geographyEnvironmental scienceSnow coverForageAerial surveyGeographyEcologyArcticBiologyRemote sensingMeteorology

Abstract

fetched live from OpenAlex

Many factors influence the abundance of migratory tundra caribou (Rangifer tarandus). To understand their interactions with caribou abundance, we need to quantify these factors. In this study, we documented the changes in habitat conditions during winter and pre-calving migration for the Bathurst Caribou herd, using remote sensing data and ground measurements. We found there was a significant decrease in forest area which has abundant lichen, the main caribou winter diet, during recent decades due to increase in burned area, which in turn was positively correlated with summer temperature. For winter forage accessibility, we examined the annual maximum snow depth and mean ice content in snow (ICIS). There was a significant increase in ICIS during 1963–2006, but no trend in the maximum snow depth. During the pre-calving migration, the percent snow cover showed large inter-annual variations but no significant trend.

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 score0.052
Threshold uncertainty score0.106

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 studies0.0010.000
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 head0.028
GPT teacher head0.211
Teacher spread0.183 · 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

Citations17
Published2012
Admission routes2
Has abstractyes

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