Bibliographic record
Abstract
Jennifer Thomson is one of the world’s leading advisors on genetically modified crops. In Food for Africa she traces, through anecdote and science, her career and the development of this area of research — from the dawn of genetic engineering in the USA in 1974, through the early stages of its testing in Europe and regulation in South Africa, to the latest developments in South Africa, where an updated Bioeconomy Strategy was approved in early 2013. As a young scientist she chose to study bacterial genetics, negotiating her way in a very male-dominated arena. It led to her path-breaking involvement in the development of GM research in South Africa — where approximately 80% of maize grown currently is genetically modified for insect and herbicide resistance — and the spread of this technology to other parts of Africa. Experiments conducted with smallholder farmers in Kenya, Uganda, Tanzania and Mozambique now mean that insect-resistant cowpea, disease-resistant bananas, virus-resistant cassava, drought-tolerant maize and vitamin-enriched sorghum can be grown in Africa successfully. This book describes a remarkable personal and scientific evolution and looks to a future in which GM technology allows for the possibility of achieving food security throughout Africa by means of staple crops grown in difficult conditions by smallholder farmers.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".