An Investigation of Coat Colour Distribution of West African Dwarf Goats
Bibliographic record
Abstract
This study was carried out to determine coat colour distribution of West African Dwarf (WAD) goat in Abeokuta North and South Local Government Areas of Ogun State, Nigeria. Three thousand, three hundred and ten (3310) WAD goats were randomly sampled consisting of 2511 (75.86%) does and 799 (24.14%) bucks. Animals were prevalently maintained on semi-intensive management. Various coat colours identified were black with highest frequency of 941 (28.42%), brown 436 (13.17%), badgerface 346 (10.45%), grey 254 (7.67%), buckskin 222 (6.70%), lateral belly 154 (4.65%), white and black 149 (4.50%), white and brown 134 (4.05%), white and black marking 105 (3.17%), black and white marking 92 (2.78%), spotted white 88 (2.66%), spotted black 80 (2.42%), blackmash 63 (1.90%), tan 61 (1.84%), white 57 (1.72%), bezoar 49 (1.48%), spotted brown 40 (1.21%), lateral stripes 33 (1.00%), with swiss marking having the lowest frequency of 6 (0.18%). The gene frequency was estimated using Hardy-Weinberg equilibrium equation which resulted as 0.70 and 0.30 for eumelanin and Phaeomelanin respectively. Chi-square (χ2) analysis was further carried out, which revealed that the difference between observed frequency and expected frequency was significant (P < 0.05) implying that the segregation in the area of study was not supported by Mendelian ratio of 3:1. High level of variation was observed in coat colour among goats in the areas studied with black coat colour being the predominant. Hence, conscious selection could be embarked upon to investigate the influence of coat colour on adaptation, thermoregulation, growth and productivity.
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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 | 0.000 |
| 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".