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Record W2140210466 · doi:10.1603/033.046.0627

Inducing Active and Passive Immunity in Sheep to Paralysis Caused by<i>Dermacentor andersoni</i>

2009· article· en· W2140210466 on OpenAlexafffund
Tim Lysyk, D. M. Veira, John P. Kastelic, W. Majak

Bibliographic record

VenueJournal of Medical Entomology · 2009
Typearticle
Languageen
FieldImmunology and Microbiology
TopicVector-borne infectious diseases
Canadian institutionsKamloops Art GalleryAgriculture and Agri-Food Canada
FundersDirectorate for Biological SciencesUniversity of Alberta
KeywordsParalysisBiologyIxodidaeTickVeterinary medicineVirologyMedicineSurgery

Abstract

fetched live from OpenAlex

Arcott sheep were evaluated as a model for studying active and passive immunity to tick paralysis caused by Dermacentor andersoni (Stiles). The incidence of tick paralysis in sheep increased from 0 at doses < or = 0.33 ticks per kg to 100% at > or = 0.8 ticks per kg. The dose required for 50% paralysis was 0.42 ticks per kg. Expressing dose as a ratio of initial ticks per unit body weight removed differences in response due to sheep weight. The interval from infestation to paralysis decreased from >12 d at 0.4 ticks per kg to <8 d at 1.3 ticks per kg. After exposure to a paralyzing doses of ticks, the incidence of paralysis varied among sheep that were naive (six of six, 100% paralysis), previously paralyzed (zero of six, 0% paralysis), and passively immunized with an intravenous treatment of 300 ml of serum from immune cattle (two of six, 33% paralysis). Sheep that were actively immunized by previous exposure had antibodies to a greater number of tick salivary antigens compared with those that were not immune. Antibodies to a 43.3-kDa antigen had 72% agreement with immunity to paralysis, and a sensitivity and specificity of 0.60 and 0.88, respectively. In conclusion, previously paralyzed sheep had developed antibodies against D. andersoni and were not susceptible to subsequent paralysis, whereas passive immunization conferred protection against paralysis in only some sheep.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.665
Threshold uncertainty score0.778

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.283
Teacher spread0.273 · 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
Published2009
Admission routes2
Has abstractyes

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