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Record W2031349063 · doi:10.1002/wsb.130

Application of a high‐resolution animal‐borne remote video camera with global positioning for wildlife study: Observations on the secret lives of woodland caribou

2012· article· en· W2031349063 on OpenAlexafffundabout
Ian D. Thompson, Mehdi Bakhtiari, Arthur Rodgers, James Α. Baker, John M. Fryxell, Edward Iwachewski

Bibliographic record

VenueWildlife Society Bulletin · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of GuelphMinistry of Natural Resources and ForestryCanadian Forest Service
FundersCanadian Forest Service
KeywordsWoodland caribouGlobal Positioning SystemWildlifeWoodlandGeographyRemote sensingEcologyComputer scienceHabitatBiologyTelecommunications

Abstract

fetched live from OpenAlex

Abstract For many species of animals, obtaining basic life‐history data is difficult and even some common aspects, such as diet choice, remain unknown. To overcome this problem, we deployed what is, to our knowledge, the first successful application of a terrestrial high‐resolution animal‐borne video camera with on‐board long‐term recording and an associated Global Positioning (GPS) unit. Five cameras recorded video and audio and associated GPS locations of woodland caribou ( Rangifer tarandus caribou ) activities during 20 weeks from March to July 2011, although the units can run for >36 weeks depending on the rate of data collection. About 6% of videos were unusable because of fogging or snow on the lens, and clarity of plant images, especially ground covers, was a problem in a few of the files but overall quality of the videos was high and identification of diet by plant species can be achieved. We present a sample of data on several aspects of previously unknown behaviors of woodland caribou from boreal forests in central Canada, including parturition and diet selection to the level of individual plant species, to illustrate the capability of these units for improving our understanding of the behaviors of large elusive animals. © 2012 The Wildlife Society.

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)0.001
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 score0.032
Threshold uncertainty score0.622

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.014
GPT teacher head0.229
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

Citations46
Published2012
Admission routes3
Has abstractyes

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