Fipa cattle in the southwestern highlands of Tanzania: molecular characterization
Bibliographic record
Abstract
This study aimed at characterising the genetic diversity of two Fipa cattle populations (Sumbawanga and Nkasi) of South-Western Tanzania, and establishing their genetic relationships with the other indigenous cattle strains (Tarime, Iringa red and Ankole) and Friesian cattle found in the area. The genetic diversity was analysed using 30 microsatellite markers. All the markers used were highly polymorphic. The Nkasi Fipa cattle exhibited the highest mean number of alleles (7.31) and mean genetic diversity (0.732) per locus, followed by Sumbawanga Fipa cattle with 7.10 mean number of alleles and 0.725 mean genetic diversity per locus, with the latter population having a very low mean inbreeding coefficient (FIS = 0.027). Three percent of the genetic diversity was due to differences among indigenous strains while the rest was due to differences among individuals within the strains. Small genetic distances (DA) were observed between Sumbawanga Fipa and Nkasi Fipa (0.032), Tarime (0.073), Iringa red (0.076) and Ankole cattle (0.086). As expected, the largest genetic distances were observed between the Friesian and all indigenous strains since this breed has a quite distinct genetic origin. In the assignment test, the proportion of animals from each group correctly assigned to their source population ranged from 55.3 percent (for Nkasi Fipa) to 100 percent (for Friesian). Despite the low genetic differentiation and genetic indistinctiveness of the Sumbawanga Fipa population from the other indigenous strains, its high genetic diversity, very low inbreeding coefficient and a threat emanating from population admixture with other indigenous strains underscore the importance of establishing appropriate conservation and management strategies for it.
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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 | 0.001 | 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".