Engineering’s Role as a Partner in the Food Supply Chain: From Farm to Plate and Beyond
Bibliographic record
Abstract
Abstract As the global population continues to grow, there will inevitably be demands for more food. Moreover, among the key elements of the United Nations’ Sustainable Development Agenda is the provision of sufficient quantities of nutritious food for all by 2030. Presently, the world actually produces enough food for all, so to meet future requirements could mean just doing more of the same in terms of food production. However, aggregate global sufficiency does not translate into individual adequacy for all, so while many have an abundance of food, others are starving. This situation is the result of a combination of many factors from armed conflicts, natural disasters, geography, and severe weather events to the state of national economies, population demographics, and the vagaries of political interventions. These causes are amplified by the sizable amounts of global food waste (FW) and post-harvest food losses (FLs). Engineering alone cannot ensure the eradication of global hunger, but engineering’s continuing involvement and improvement at the start of the food supply chain (FCS), i.e., the sowing, growing, and harvesting, could play a major role. Unfortunately, there is a general lack of awareness of how engineering has been, and continues to be, involved in the production and acquisition of food to its processing and consumer availability, i.e., farm to plate. This paucity of awareness is also evident among many engineering communities. In this chapter, some insights are provided into engineering’s role in agriculture together with some remarks on sustainable food production
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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) | 0.000 | 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) | 0.001 | 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".