Characterization of Maasai Goats in Extensive Production System in Northern Tanzania: Description of Phenotype, Reproductive and Productive Performance
Bibliographic record
Abstract
The study was designed to offer information on phenotype, reproductive and productive features of Maasai goats for amelioration in breeding programs utilizing these animals. Live measurements and qualitative traits were collected from 75 bucks and 165 does. A detailed survey was used to acquire information on reproductive and productive traits. Data were analyzed using descriptive statistics and General Linear Model procedures for age and sex as main effects. The results revealed that body weight, heart girth, chest depth, rump width, head length, head width and horn length differed (p < 0.05) among age groups. Body weight increased from young to old age and ranged from 25.83 kg to 30.34 kg. Body length, heart girth, head length, head width and cannon bone length were significantly (p < 0.05) higher in bucks than does. Plain white was the foremost coat color manifested. Nearly, 76% of bucks and 78.2% of does had straight short hairs. Beards were in 80% of bucks and 9.1% of does. All goats had short and erect ears; whereas, more than 88% had horns, 48% of the horns were curved, 33.1% lateral and 18.1% straight. Almost 76% and 83.6% of the horns in bucks and does, respectively, were pointing backward. In both sexes, facial profile was mainly concave, back profile was straight and wattles were absent. Twinning rate, age at sexual maturity, age at first kidding, kidding interval and lactation length were averaged at 8.7%, 11 months, 16.7 months, 7.7 months and 82.3 days; whereas, pre weaning kid survival rate was 77.1% and 79.9% for dry and rainy season, respectively. The strain was comparatively similar to most African indigenous goat populations. Thus, the strain can be utilized through selection for the traits preferred for the arid and semi-arid tropics.
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 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 | 0.000 |
| 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".