Advances in Plant Breeding, Polemics of Genetically Modified Crops and Biosafety Frameworks in Ethiopia
Bibliographic record
Abstract
Ethiopia is one of the top African countries with fast population growth that requires technological interventions for improving agricultural production. Agriculture is entirely the source of food or nutrition security, raw material for agro-industries and export commodities for the country. The abrupt population increase augmented with challenges derived by climate change and newly emerging problems necessitate the use of modern plant breeding techniques. This paper provides insights of advancements in new crop improvement research, discourses associated with genetically engineered crops and biosafety frameworks in the country. Ethiopia has begun evaluation and use of genetically modified (GM) crops. The classical agricultural researches are being undertaken for more than five decades but require embracing modern tools to better address agricultural challenges. As compared to conventionally developed elite varieties, GM crops are found to be more advantageous based on their traits of interest in various ways. In handling GM research, there was no compromise on the biosafety procedures and regulations of the country. Bollworm resistant cotton, insect resistant and drought tolerant maize have already been evaluated incompliance with the country’s biosafety framework and released for general use while few GM crops are still under confined or contained evaluations. Opponents are emerging in the country with the adoption of the technology and misinformation is undergoing using various media outlets. Public research and regulatory institutes have been providing evidence based information using all possible means. Continuous public awareness enhancement is equally important with the adaptation and use of new technologies.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".