Application of DNA markers to identify the individual‐specific hosts of tsetse feeding on cattle
Bibliographic record
Abstract
Primer sets for five different ungulate loci were used to obtain individual microsatellite DNA profiles for 29 Mashona cattle from a herd in Zimbabwe. There were 3-13 alleles for each locus and, using the entire suite of five loci, each animal within the herd, including closely related individuals, could be unequivocally distinguished. Wild-caught Glossina pallidipes Austen (Diptera: Glossinidae) were fed on specific cattle and the bloodmeal was profiled 0.5-72 h after feeding. The individual specific sources of the bloodmeals, including mixe meals produced by allowing tsetse to feed on two different cattle, were reliabl identified up to 24 h after feeding. The technique was used in field studies of hos selection by G. pallidipes and G. morsitans morsitans Westwood (Diptera Glossinidae) attracted to pairs of cattle. When the pair comprised an adult and a calf, 100% of meals were from the adult. For some pairs of adult cattle, tsetse were biased significantly towards feeding on one animal, whereas for other pairs there was no such bias. In general, feeding was greater on the animal known to have lower rate of host defensive behaviour. Results suggest that relatively slight differences in the inherent defensive behaviour of cattle produce large difference in host-specific feeding rates when the hosts are adjacent. For flies attracted to pair of cattle, < 2% contained blood from both hosts. The DNA profiling technique will be useful in studying the epidemiology of vector-borne diseases of livestock.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".