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Record W2007824262 · doi:10.1139/z10-086

Partial seasonal migration in high-arctic Svalbard reindeer (Rangifer tarandus platyrhynchus)

2010· article· en· W2007824262 on OpenAlexvenueno aff
Brage Bremset Hansen, Ronny Aanes, Bernt‐Erik Sæther

Bibliographic record

VenueCanadian Journal of Zoology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsnot available
FundersNorges ForskningsrådNorges Teknisk-Naturvitenskapelige Universitet
KeywordsRange (aeronautics)BiologyEcologyArcticLichenPopulationForagingHabitatPredationAbundance (ecology)Demography

Abstract

fetched live from OpenAlex

We examined seasonal range use and calving success in wild Svalbard reindeer ( Rangifer tarandus platyrhynchus Vrolik, 1829) on two contrasting ranges separated by risky barriers (open sea, thin sea ice, and glaciers). One (“poor”) range had a depleted lichen resource and negative reindeer population trend, whereas the neighbouring (“rich”) range was recently occupied with initially high lichen abundance and positive population trend. Winter foraging conditions limit survival and reproduction in this predator-free system and lichens are the preferred winter food by reindeer. Accordingly, marked female reindeer that switched between the ranges (“migrants”) spent most winters on the lichen-rich range, yet most summers on the poor range (possibly owing to fidelity to calving area). While facing high mortality risk along the migration route, migrants spending the winter on the rich range and subsequent summer on the poor range had improved calving success compared with residents on the poor range. The partial seasonal migration pattern diminished as lichens were reduced and reindeer carrying capacity reached on the rich range. Besides this apparent density-dependence in migratory behaviour, spatial strategy seems shaped by past experience and trade-offs between current survival (safe habitat) and future reproduction (food-rich habitat).

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 score0.524
Threshold uncertainty score0.996

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)0.0050.000

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 head0.190
Teacher spread0.184 · 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

Citations50
Published2010
Admission routes1
Has abstractyes

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