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Record W3096107391 · doi:10.1080/15230430.2020.1827577

Changes in organ size and nutrient reserves of arctic terns (<i>Sterna paradisaea</i>) breeding near a High Arctic polynya

2020· article· en· W3096107391 on OpenAlexafffundabout
Julia E. Baak, Jennifer F. Provencher, Mark L. Mallory

Bibliographic record

VenueArctic Antarctic and Alpine Research · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsEnvironment and Climate Change CanadaAcadia University
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsSternaArcticTernGizzardSeabirdBiologyNutrientForagingSeasonal breederCharadriiformesEcologyFisheryPredation

Abstract

fetched live from OpenAlex

The arctic tern (Sterna paradisaea) is a ubiquitous migratory seabird of the High Arctic, currently thought to be in decline in most of the circumpolar world, but surprisingly little is known of its biology at high latitudes. We studied organ size and nutrient reserves of arctic terns breeding beside a High Arctic polynya in Nunavut, Canada, from their arrival at the colony into the chick-rearing period. Both males and females had a decrease in gizzard size through breeding, with gizzard mass during chick-rearing 39 percent lower than on arrival at the breeding grounds. Through the duration of the breeding season, heart, liver, and small intestine showed little change, but females had higher fat and protein stores than males. Terns from this colony likely have increasing fat levels and high body condition due to proximity to a highly productive polynya, where terns appear to gain more energy than they expend during foraging trips. This suggests that though terns at this colony may be near the northern limit of their range, local conditions have a strong impact on organ and nutrient reserve dynamics of these arctic seabirds.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.040
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.270
Teacher spread0.232 · 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

Citations3
Published2020
Admission routes3
Has abstractyes

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