Observation of Genetic Markers for Resistance to Gastrointestinal Parasites in Goats
Bibliographic record
Abstract
In many small-scale farmers and grazing systems, the biggest problem that goats face is not the shortage of feed, but the health risks and production losses caused by gastrointestinal parasites. Although such problems have long existed, they are now even more troublesome - the old method of relying on deworming drugs to solve them is becoming less and less effective at present. On the one hand, drug resistance is intensifying; on the other hand, the pressure of environmental protection and sustainability also forces people to rethink their strategies. This study systematically explored the genetic basis of goat resistance to parasites, with a focus on analyzing key genetic markers related to immune response, intestinal barrier function, and inflammatory regulation. It also reviewed the application progress of different types of markers such as microsatellites (SSR), single nucleotide polymorphisms (SNPS), and candidate genes in resistance research. And strategies such as QTL mapping, genome-wide association analysis (GWAS), and gene expression analysis were evaluated. Through case comparisons of breeds such as Boer goats, native goats, Indian Jamunapari and African Red Maasai, this study reveals the diversity of resistance genes among breeds and their specific characteristics. This study emphasizes the significance of strengthening multi-group joint analysis and data sharing, providing a theoretical basis for building an ecological and sustainable goat anti-parasitic breeding system.
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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 | 0.001 | 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".