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Record W2200613020 · doi:10.1890/es15-00174.1

Selection for forage and avoidance of risk by woodland caribou (<i>Rangifer tarandus caribou</i>) at coarse and local scales

2015· article· en· W2200613020 on OpenAlexafffundabout
Madeleine McGreer, Erin E. Mallon, Lucas M. Vander Vennen, Philip A. Wiebe, James Α. Baker, Glen S. Brown, Tal Avgar, Jevon Hagens, Andrew M. Kittle, Anna Mosser, Garrett M. Street, Doug E. B. Reid, Arthur Rodgers, Jennifer L. Shuter, Ian D. Thompson, Merritt J. Turetsky, Steven G. Newmaster, Brent R. Patterson, John M. Fryxell

Bibliographic record

VenueEcosphere · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsCanadian Forest ServiceMinistry of Natural Resources and ForestryUniversity of AlbertaUniversity of Guelph
FundersCanadian Forest ServiceNatural Sciences and Engineering Research Council of CanadaU.S. Forest ServiceMinistry of Natural Resources
KeywordsWoodland caribouSelection (genetic algorithm)WoodlandForageSpatial ecologyEcologyRange (aeronautics)Scale (ratio)PredationTemporal scalesTaigaBorealGeographyBiologyCartographyComputer scienceEngineeringMachine learning

Abstract

fetched live from OpenAlex

The relationship between selection at coarse and fine spatiotemporal spatial scales is still poorly understood. Some authors claim that, to accommodate different needs at different scales, individuals should have contrasting selection patterns at different scales of selection, while others claim that coarse scale selection patterns should reflect fine scale selection decisions. Here we examine site selection by 110 woodland caribou equipped with GPS radio‐collars with respect to forage availability and predation risk across a broad gradient in availability of both variables in boreal forests of Northern Ontario. We tested whether caribou selection for forage and avoidance of risk was consistent between coarse (seasonal home range) and fine scales of selection. We found that local selection patterns predicted coarse scale selection patterns, indicating a close relationship between the drivers of selection at both spatial scales.

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)0.000
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 score0.062
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

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 head0.006
GPT teacher head0.197
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

Citations30
Published2015
Admission routes3
Has abstractyes

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