Planting seeds for the future of food
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".