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Record W2153769777 · doi:10.5376/ijssr.2012.02.0001

Host-Parasite Interactions between Birds and Feather Mites

2012· article· en· W2153769777 on OpenAlexvenueno aff
Tawanda Tarakini, Mikis Bastian

Bibliographic record

VenueInternational Journal of Super Species Research · 2012
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBird parasitology and diseases
Canadian institutionsnot available
Fundersnot available
KeywordsFeatherParasite hostingHost (biology)BiologyZoologyEcologyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

There is current uncertainty on whether feather mites are a cause or consequence of poor body condition in birds. We aimed at investigating the bird-mite relationships and elucidating the functional significance of feather mites on birds found on the Urra Field station, Sorbas, in Almeria province, south-east Spain. We captured birds using mist nest and assessed birds for body condition (weight, fat and pectoral muscles), mite distribution on the wings and tested for diurnal changes in mite abundance. The Kruskal-Wallis test was used to test for differences in mite abundance across species, sex, age and to test for differences in mite distribution on wings across species while the Fisher`s exact test was used to test for diurnal mite abundance. There were no significant differences in mite abundance between males and females in blackcaps, house sparrows, Sardinian and Willow warblers. There were significant differences in the abundance of mites on Blackcaps, house sparrows, sardinian and willow warblers. This study was carried out just before the breeding season, thus the juveniles may have been “mite-contaminated” by adults during the winter. Also, blackcaps could potentially be carrying different mite species, collected enroute during their migration. With more observational data over different time of day and seasons, investigations could be carried out to describe mite movements depending on varying environmental factors.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.236
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.100
GPT teacher head0.421
Teacher spread0.321 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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