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Record W2134497235 · doi:10.1093/conphys/cov028

Leucocyte profiles of Arctic marine birds: correlates of migration and breeding phenology

2015· article· en· W2134497235 on OpenAlexafffund
Mark L. Mallory, Catherine M. Little, Ellen Boyd, Jennifer R. Ballard, Kyle H. Elliott, H. Grant Gilchrist, J. Mark Hipfner, Aevar Petersen, Dave Shutler

Bibliographic record

VenueConservation Physiology · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsCarleton UniversityEnvironment and Climate Change CanadaMcGill UniversityAcadia University
FundersEnvironment CanadaNatural Sciences and Engineering Research Council of CanadaResearch Nova Scotia
KeywordsBiologyArcticEcologyPhenologySeasonal breederTernThe arcticZoologyOceanography

Abstract

fetched live from OpenAlex

Most Arctic marine birds are migratory, wintering south of the limit of annual pack ice and returning north each year for the physiologically stressful breeding season. The Arctic environment is changing rapidly due to global warming and anthropogenic activities, which may influence the timing of breeding in relation to arrival times following migration, as well as providing additional stressors (e.g. disturbance from ships) to which birds may respond. During stressful parts of their annual cycle, such as breeding, birds may reallocate resources so that they have increased heterophil-to-lymphocyte ratios in their white blood cell (leucocyte) profiles. We analysed leucocyte profiles of nine species of marine birds to establish reference ranges for these species in advance of future Arctic change. Leucocyte profiles tended to cluster among taxonomic groups across studies, suggesting that reference values for a particular group can be established, and within species there was evidence that birds from colonies that had to migrate farther had higher heterophil-to-lymphocyte ratios during incubation than those that did not have to travel as far, particularly for species with high wing loading.

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 score0.175
Threshold uncertainty score0.289

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.239
Teacher spread0.211 · 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

Citations16
Published2015
Admission routes2
Has abstractyes

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