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Record W2116764018 · doi:10.1017/s0952836903004552

Seasonal movement patterns of woodland caribou (<i>Rangifer tarandus caribou</i>)

2004· article· en· W2116764018 on OpenAlexafffundabout
Steven H. Ferguson, Philip C. Elkie

Bibliographic record

VenueJournal of Zoology · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsMinistry of Natural Resources and ForestryFisheries and Oceans Canada
FundersMinistry of Natural Resources
KeywordsWoodland caribouRange (aeronautics)BiologySeasonalityEcologyGeographyHabitat

Abstract

fetched live from OpenAlex

Abstract For species conservation, defining seasons according to an animal's perception and consideration of seasonal differences in its use of space is required. First, a statistical approach was used to determine seasons based on rates of movement of radio‐collared caribou Rangifer tarandus caribou . Using polynomial regression, five seasons were delineated by inflection points that demarcate changes in rates of movement. Male and female caribou did not differ in timing and number of seasons. Second, the seasons so defined were used to describe the use of space in 33 caribou in north‐western Ontario, Canada. Greatest rates of movement occurred during spring and early winter migration when caribou had both the largest range size and largest distance between seasonal ranges. The general direction of movement was west in spring and east early in winter, although variation occurred among caribou and years. A drop in rate of movement lasting about 3 days may indicate calving. Caribou were most dispersed and occupied the smallest seasonal range during the calving season. The animals showed strong fidelity to calving areas in contrast to the highly variable year‐to‐year location of winter ranges. Understanding the seasons of animal movement patterns, and applying this understanding when developing plans for forest management, may help reduce adverse effects of forest harvesting on highly mobile animals such as woodland caribou.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score1.000

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 head0.007
GPT teacher head0.209
Teacher spread0.201 · 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.

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

Citations110
Published2004
Admission routes3
Has abstractyes

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