Contributions of Genome Editing Technologies Towards Improved Nutrition, Environmental Sustainability and Poverty Reduction
Bibliographic record
Abstract
The Sustainable Development Goals (SDGs) were launched in 2015, with the top three goals being poverty eradication, improved food security and increased human health. All 17 SDGs have a target achievement date of 2030. These are ambitious and inspirational goals that require substantial innovation and technology adoption for successful achievement. Innovations in plant breeding have substantially contributed to transforming the efficiency of food production since the mid 20th century, with innovations emerging in the current millennium demonstrating enhanced potential to improve crop yields, the nutritional values of food crops and environmental impacts. These outcomes underpin several SDGs, but in particular the first three. As climate change is expected to become increasingly variable, with greater impacts on agriculture, the ability to ensure increased food production is going to be increasingly important, as higher yields directly contribute to reducing poverty. This article reviews recent reports of potential contributions from genome editing technologies in terms of increased yield, enhanced nutrition and greater sustainability, highlighting their importance for achieving the leading three SDGs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".