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Record W2078920408 · doi:10.7589/0090-3558-44.3.687

Nasal Bots and Lice from White-tailed Deer in Southern Alberta, Canada

2008· article· en· W2078920408 on OpenAlexaffabout
Douglas D. Colwell, Dawn Gray, Kim Morton, M. J. Pybus

Bibliographic record

VenueJournal of Wildlife Diseases · 2008
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBird parasitology and diseases
Canadian institutionsAlberta Environment and Protected AreasAgriculture and Agri-Food Canada
Fundersnot available
KeywordsBiologyOdocoileusInfestationVeterinary medicineLarvaLouseInstarHerdAnimal scienceZoologyEcologyAgronomyMedicine

Abstract

fetched live from OpenAlex

Heads of 64 white-tailed deer (Odocoileus virginianus) fawns, harvested in the vicinity of Magrath, Alberta, Canada, (49 degrees 24'782''N, 112 degrees 52'113''W) were examined for the presence of nasal bots and lice. The deer were collected between 8-30 January 2004 as part of a government-approved herd reduction protocol. The entire surface of each head was scanned visually for the presence of lice. Each head was split longitudinally, and the nasal passages, sinuses, and ethmoid region were washed for recovery of nasal bots. First instar Cephenemyia spp. were recovered from 17 heads (27%). Intensity of infestation ranged from 1-18 larvae (mean intensity 4.8). Among fawns, there were no significant differences in prevalence or mean intensity between the sexes. Two species of nasal bots were identified. Smaller larvae, tentatively identified as C. jellisoni, were present in 16 of 17 infested deer while larger specimens, tentatively identified as C. phobifera, were found in four deer; and in three of the four it co-occurred with C. jellisoni. The presence of C. phobifera in Alberta would represent a range extension for this species, which has not been known to occur west of North Dakota. Thirty-one fawns (48%) were infested with the sucking louse Solenopotes ferrisi. One infested fawn also had one specimen of the chewing louse, Tricholiopeurus lipeuroides.

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 score0.121
Threshold uncertainty score0.864

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 studies0.0000.000
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 head0.007
GPT teacher head0.205
Teacher spread0.198 · 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

Citations9
Published2008
Admission routes2
Has abstractyes

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