Bibliographic record
Abstract
Abstract Wondmeneh Esatu Woldegiorgiss (2015). Genetic improvement in indigenous chicken of Ethiopia. PhD thesis, Wageningen University, the Netherlands This thesis considered various approaches to study the potential for improvement of village poultry production system using improved indigenous chicken. The approaches were structured survey questionnaire, village poultry simulation model (VIPOSIM), Heckman two-step model (econometric model), and experiments involving laboratory and field. First factors that determine the probability and intensity of adoption of exotic chickens were assessed. The probability of adopting exotic chickens was found to be positively affected by access to an off-farm income and negatively by livestock income. The intensity of adoption was negatively affected by being male household head, having a larger farm size, and having livestock income. Then, perceptions of farmers towards village poultry and impacts of interventions on flock and economic performance were assessed. Farmers’ perceptions affected their decisions about implementation of interventions, and interventions increased productivity but only in a few cases the increased revenues outweighed the additional costs. Subsequently, the evaluation of the breeds was conducted by comparing the natural antibody and productivity of improved indigenous chicken with crossbred, commercial and unimproved indigenous chickens. The results revealed that not only the NAb levels but also the effect of NAbs on survival differ between indigenous and improved breeds. NAb levels are associated with survival in commercial layer breed, but reduced survival in indigenous chickens placed in confinement. Improved indigenous chickens showed higher performance than unimproved one for all traits measured on-station, but remains lighter and developed more into a laying type than meat through the short-term selective breeding program. Overall, the present studies indicate that interventions need to be tailored towards the local situation to ensure that they lead not only to improved productivity but also to improved income.
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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 | 0.000 |
| 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".