Inducing Active and Passive Immunity in Sheep to Paralysis Caused by<i>Dermacentor andersoni</i>
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".