MétaCan
Menu
Back to cohort
Record W2897059504 · doi:10.1038/s41467-018-06673-5

Fuelling conditions at staging sites can mitigate Arctic warming effects in a migratory bird

2018· article· en· W2897059504 on OpenAlexaff
Eldar Rakhimberdiev, Sjoerd Duijns, Julia Karagicheva, C.J. Camphuysen, Andre van Loon, Arnold Wijker, G.O. Keijl, Henk Levering, Leo Heemskerk, Luc Knijnsberg, Marc van Roomen, Paul Ruiters, Piet Admiraal, Piet Veldt, Richard Reijnders, Walter Beentjes, Anne Dekinga, Rob Dekker, Anatoly Gavrilov, Job ten Horn, Joop Jukema, Anatoly A. Saveliev, Mikhail Soloviev, T. Lee Tibbitts, Jan A. van Gils, Theunis Piersma

Bibliographic record

VenueNature Communications · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsCarleton University
FundersKoninklijk Nederlands Instituut voor Onderzoek der ZeeWaddenfondsNederlandse Organisatie voor Wetenschappelijk Onderzoek
KeywordsArcticThe arcticGlobal warmingEnvironmental scienceEcologyClimate changeBiologyOceanographyGeology

Abstract

fetched live from OpenAlex

Under climate warming, migratory birds should align reproduction dates with advancing plant and arthropod phenology. To arrive on the breeding grounds earlier, migrants may speed up spring migration by curtailing the time spent en route, possibly at the cost of decreased survival rates. Based on a decades-long series of observations along an entire flyway, we show that when refuelling time is limited, variation in food abundance in the spring staging area affects fitness. Bar-tailed godwits migrating from West Africa to the Siberian Arctic reduce refuelling time at their European staging site and thus maintain a close match between breeding and tundra phenology. Annual survival probability decreases with shorter refuelling times, but correlates positively with refuelling rate, which in turn is correlated with food abundance in the staging area. This chain of effects implies that conditions in the temperate zone determine the ability of godwits to cope with climate-related changes in the Arctic.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.963

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.013
GPT teacher head0.283
Teacher spread0.269 · 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.

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

Citations110
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueNature CommunicationsSame topicAvian ecology and behaviorFrench-language works237,207