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Record W1987629191 · doi:10.1139/z08-074

The case of the blood-covered egg: ectoparasite abundance in an arctic goose colony

2008· article· en· W1987629191 on OpenAlexafffundvenueabout
Vanessa B. Harriman, Ray T. Alisauskas, Gary A. Wobeser

Bibliographic record

VenueCanadian Journal of Zoology · 2008
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBird parasitology and diseases
Canadian institutionsUniversity of SaskatchewanGenome PrairieEnvironment and Climate Change Canada
FundersPontificia Universidad Católica del EcuadorUniversity of Saskatchewan
KeywordsGooseBiologyFleaNest (protein structural motif)WaterfowlAbundance (ecology)AnatidaeEcologyBrantaZoologyHabitatHost (biology)

Abstract

fetched live from OpenAlex

Since 1991, blood-covered eggs have been noted in nests of Ross’s ( Chen rossii (Cassin, 1861)) and lesser snow ( Chen caerulescens caerulescens (L., 1758)) geese at the Karrak Lake colony, Nunavut, Canada. Fleas ( Ceratophyllus vagabundus vagabundus (Boheman, 1866)) were subsequently observed to be associated with goose nests containing eggs covered with dried blood. We examined prevalence of blood presence on goose eggs and extent of egg coverage with blood in goose nests from 2001 to 2004. Flea abundance in nests was estimated in 2003 and 2004, and was strongly correlated with the proportion of goose egg surface covered by blood, suggesting that degree of blood coverage was a suitable index of flea abundance. Extent of blood fluctuated annually and was correlated with both host characteristics and host habitat factors. Nest bowls used by geese in previous years contained more fleas than did new nest bowls, and fleas were more abundant in older areas of the colony. Flea abundance increased with goose clutch size and was highest in rock and birch habitats. Ceratophyllus vagabundus vagabundus appears to be a new parasite of geese at Karrak Lake; flea abundance may change in response to increased availability of favorable habitat, which is expected if local climate warms.

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 score0.345
Threshold uncertainty score0.995

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.241
Teacher spread0.228 · 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

Citations13
Published2008
Admission routes4
Has abstractyes

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