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Record W2154638857 · doi:10.1525/cond.2010.090050

Why Roost at the Same Place? Exploring Short-Term Fidelity in Staging Snow Geese

2010· article· en· W2154638857 on OpenAlexafffundabout
Arnaud Béchet, Jean‐François Giroux, Gilles Gauthier, Marc Bélisle

Bibliographic record

VenueOrnithological Applications · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsUniversité de SherbrookeUniversité LavalCenter for Northern StudiesUniversité du Québec à Montréal
FundersInstitute for Wetland and Waterfowl Research, Ducks Unlimited CanadaUniversité du Québec à Montréal
KeywordsForagingForageFidelityEcologyGeographySeasonal breederSnowBiologyMeteorologyComputer science

Abstract

fetched live from OpenAlex

When a communal roost is large relative to foraging distances, variance in foraging success may affect the positioning of the birds within the roost and we should expect fidelity to positions that improve foraging success. We explored fidelity of Snow Geese (Chen caerulescens) to three sections of a 5-km2 roost in flooded lowlands during their spring stopover in Quebec. From 1998 to 2000, we located 166 radio-tagged geese on 1077 occasions. Fidelity rates were higher than expected by chance in all sections in 1998, in two in 2000, but in none in 1999. Fidelity increased with the number of birds using a section, suggesting a positive effect of conspecific attraction. We tracked 292 foraging trips of 108 radio-tagged geese; birds from different sections tended to forage in specific directions. Average distance to foraging sites saved by appropriate choice of a section varied between 7 and 17%, depending on the section. However, distance traveled over 2 successive days did not decrease when geese switched from roosting in one section to another, suggesting that minimization of foraging-trip distance may stem simply from the spatial organization of foraging trips in order to reduce travel distance to food patches. Higher fidelity rates were associated with shorter travel distance in only one section of the roost, and dominant birds arriving early in the season tended to be more faithful to this section. We conclude that conspecific attraction, reduction in travel costs to foraging sites, and individual variation in dominance determine roost positioning and fidelity concurrently.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.045
GPT teacher head0.283
Teacher spread0.238 · 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; both teacher heads agree on what is shown here.

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

Citations8
Published2010
Admission routes3
Has abstractyes

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