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Record W1978059676 · doi:10.7557/2.18.5.1551

Fractal measures of female caribou movements

2011· article· en· W1978059676 on OpenAlexaboutno aff
Steven H. Ferguson, W. James Rettie, François Messier

Bibliographic record

VenueRangifer · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
Fundersnot available
KeywordsBiological dispersalFractal dimensionWoodland caribouPredationEcologyGeographyBorealHabitatRange (aeronautics)PopulationHome rangeTaigaFractalBiologyMathematicsDemography

Abstract

fetched live from OpenAlex

Understanding caribou movement during short-term searches for specific habitats, potential mates, and refugia against predators can help resolve ecological questions on how individual caribou perceive their environment. We used measures of fractal dimension and standardized pathlength to compare the movement pathways of female caribou. Satellite telemetry locations were collected over a 2-year study, March 1994 to mid-May 1996, for a caribou population in central Saskatchewan living in the southern boreal forest. Female caribou displayed more random searching behaviour during winter and more regular dispersal movements during early winter/spring and autumn periods. Females with a calf showed no difference in movement pattern (fractal dimension) relative to females without a calf but their standardized path length was shorter. We discuss the advantages of using fractal dimension as a measure of the tortuosity of movement pathways and how changes in fractal dimension over a range of scales can define domains of consistent ecological processes.

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)0.002
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.059
GPT teacher head0.238
Teacher spread0.179 · 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
Published2011
Admission routes1
Has abstractyes

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