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Record W2996641250 · doi:10.4039/tce.2019.69

Stability of chewing louse (Phthiraptera: Amblycera and Ischnocera) populations infesting great horned owls (Aves: Strigidae)

2019· article· en· W2996641250 on OpenAlexaffabout
Robert J. Lamb, Terry D. Galloway

Bibliographic record

VenueThe Canadian Entomologist · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBird parasitology and diseases
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsLouseBiologyAbundance (ecology)PopulationZoologyEcologyNymphDemography

Abstract

fetched live from OpenAlex

Abstract The annual abundance of chewing lice (Phthiraptera) was recorded on great horned owls ( Bubo virginianus (Gmelin), Aves: Strigidae) from 1994 to 2015 in Manitoba, Canada. Kurodaia magna Emerson (Amblycera: Menoponidae) had a mean annual abundance about half that for Strigiphilus oculatus (Rudow) (Ischnocera: Philopteridae). Mean intensity, rather than prevalence, explained the variation in annual abundance. Temporal variation (measured as population variability) in abundance and mean intensity were high and similar (0.62–0.67), but lower for nymph to female ratio (0.36–0.38). Temporal variation of prevalence and sex ratio were higher for K. magna (0.34–0.35) than for S. oculatus (0.21–0.22), and typical for other louse species. The high temporal variability for abundance and mean intensity suggest lower year-to-year stability than exhibited by other chewing lice, but over 80% of this variability was due to sampling error resulting from small sample sizes in some years and extreme intensities in the aggregated distributions of intensity. The remaining variation, < 20%, revealed no significant differences in annual abundance or mean intensity among years, and therefore stable populations over 22 years. Populations of 12 species of chewing lice show lower temporal variability and therefore greater stability than three other insect taxa.

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 score0.072
Threshold uncertainty score0.853

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)0.0010.000

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 head0.045
GPT teacher head0.268
Teacher spread0.224 · 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

Citations3
Published2019
Admission routes2
Has abstractyes

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