Planting seeds for the future of food
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The health and wellbeing of future generations will depend on humankind's ability to deliver sufficient nutritious food to a world population in excess of 9 billion. Feeding this many people by 2050 will require science-based solutions that address sustainable agricultural productivity and enable healthful dietary patterns in a more globally equitable way. This topic was the focus of a multi-disciplinary international conference hosted by Nestlé in June 2015, and provides the inspiration for the present article. The conference brought together a diverse range of expertise and organisations from the developing and industrialised world, all with a common interest in safeguarding the future of food. This article provides a snapshot of three of the recurring topics that were discussed during this conference: soil health, plant science and the future of farming practice. Crop plants and their cultivation are the fundamental building blocks for a food secure world. Whether these are grown for food or feed for livestock, they are the foundation of food and nutrient security. Many of the challenges for the future of food will be faced where the crops are grown: on the farm. Farmers need to plant the right crops and create the right conditions to maximise productivity (yield) and quality (e.g. nutritional content), whilst maintaining the environment, and earning a living. New advances in science and technology can provide the tools and know-how that will, together with a more entrepreneurial approach, help farmers to meet the inexorable demand for the sustainable production of nutritious foods for future generations.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it