Resilient foods for preventing global famine: a review of food supply interventions for global catastrophic food shocks including nuclear winter and infrastructure collapse
Bibliographic record
Abstract
Global catastrophic threats to the food system upon which human society depends are numerous. A nuclear war or volcanic eruption could collapse agricultural yields by inhibiting crop growth. Nuclear electromagnetic pulses or extreme pandemics could disrupt industry and mass-scale food supply by unprecedented levels. Global food storage is limited. What can be done?. This article presents the state of the field on interventions to maintain food production in these scenarios, aiming to prevent mass starvation and reduce the chance of civilizational collapse and potential existential catastrophe. The potential for rapid scaling, affordability, and large-scale deployment is reviewed for a portfolio of food production methods over land, water, and industrial systems. Special focus is given to proposing avenues for further research and technology development and to collating policy proposals. Maintaining international trade and prioritizing crops for food instead of animal feed or biofuels is paramount. Both mature, proven methods (crop relocation, plant-residue- and grass-fed ruminants, greenhouses, seaweed, fishing, etc.) and novel resilient foods are characterized. A future research agenda is outlined, including scenario characterization, policy development, production ramp-up and economic analyses, and rapid deployment trials. Governments could implement national plans and task forces to address extreme food system risks, and invest in resilient food solutions to safeguard citizens against global catastrophic food failure.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".