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Record W2014656592 · doi:10.3354/meps198283

Variability in foraging in response to changing prey distributions in rhinoceros auklets

2000· article· en· W2014656592 on OpenAlexafffund
GK Davoren

Bibliographic record

VenueMarine Ecology Progress Series · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsSt. John’s Health Sciences CentreUniversity of VictoriaMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaMinistry of EnvironmentParks CanadaBird Studies Canada
KeywordsForagingPredationEcologyAbundance (ecology)TransectRhinocerosBiological dispersalBiologyBehavioral ecologyGeographyPopulationDemography

Abstract

fetched live from OpenAlex

Variable time budgets and foraging behaviour were observed in a marine diving bird, the rhinoceros auklet Cerorhinca monocerata, in response to intraseasonal and interannual variations in prey abundance and distribution. Few studies have simultaneously measured the spatial dispersal of seabirds at sea, time budgets at sea and prey abundance and distribution. Time budgets and foraging behaviour were deterrnined through visual scans. Prey abundance, estimated hydroacoustically during marine transects, was similar among years, but prey was dispersed over larger spatial areas in 1997 than in 1995 and 1996. Rhinoceros auklets were also dispersed over larger spatial areas in 1997 and fewer mixed-species feeding flocks were formed. In 1997, rhinoceros auklets increased the time spent foraging, decreased the recovery periods between successive dives, and were more strongly associated with prey at larger spatial scales. This suggested that auklets were working hard while foraging but were less successful at locating and maintaining contact with prey when prey was more dispersed. In 1996, there was a period (June 13 to 20) when fish schools were common near the surface, during which auklets spent more time foraging and formed more feeding flocks. This suggested that auklets were working hard to take advantage of this readily available prey. This paper iilustrates the importance of behavioural plasticity and time budget flexibility for seabirds Living in highly variable environments.

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)0.002
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 score0.061
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.009
GPT teacher head0.245
Teacher spread0.236 · 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

Citations58
Published2000
Admission routes2
Has abstractyes

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