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Feather mites and birds: an interaction mediated by uropygial gland size?

2007· article· en· W2169645343 on OpenAlexafffund
Ismael Galván, Emilio Barba, Rubén Piculo, Josep Cantó, Verónica Cortés, Juan S. Monrós, Francisco Borrás Atiénzar, H. C. Proctor

Bibliographic record

VenueJournal of Evolutionary Biology · 2007
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBird parasitology and diseases
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyPasserineFeatherMiteZoologyAcariPlumageInterspecific competitionEcologySparrow

Abstract

fetched live from OpenAlex

Feather mites (Arachnida: Acari: Astigmata) feed mainly on secretions of the uropygial gland of birds. Here, we use analyses corrected for phylogeny and body size to show that there is a positive correlation between the size of this gland and mite abundance in passerine birds at an interspecific level during the breeding season, suggesting that the gland mediates interactions between mites and birds. As predicted on the basis of hypothesized waterproofing and antibiotic functions of uropygial gland secretions, riparian/marsh bird species had larger glands and higher mite loads than birds living in less mesic terrestrial environments. An unexpected pattern was a steeper relationship between mite load and gland size in migratory birds than in residents. If moderate mite loads are beneficial to a host but high loads detrimental, this could create complex selection regimes in which gland size influences mite load and vice versa. Mites may exert selective pressures on gland size of their hosts that has resulted in smaller glands among migratory bird species, suggesting that smaller glands may have evolved in these birds to attenuate a possible detrimental effect of feather mites when present in large numbers.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.278
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), 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

Citations93
Published2007
Admission routes2
Has abstractyes

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