The United Nations Sustainable Development Goal 2 (SDG 2) is to achieve zero hunger by 2030. We have designed Persephone, a machine learning model, to support a diverse volunteer network of 77 researchers from 23 countries engaged in creating interdisciplinary evidence syntheses in support of SDG 2. Such evidence syntheses, whatever the specific topic, assess original studies to determine the effectiveness of interventions. By gathering and summarizing current evidence and providing objective recommendations they can be valuable aids to decision-makers. However, they are time-consuming; estimates range from 18 months to three years to produce a single review. Persephone analysed 500,000 unstructured text summaries from prominent sources of agricultural research, determining with 90% accuracy the subset of studies that would eventually be selected by expert researchers. We demonstrate that machine learning models can be invaluable in placing evidence into the hands of policymakers.

联合国可持续发展目标2（SDG 2）计划到2030年实现零饥饿。我们设计了机器学习模型Persephone，以支持来自23个国家的77名研究人员的多元化志愿者网络，这些研究人员致力于创建跨学科的证据合成，以支持可持续发展目标2.不论具体的主题如何，这些证据综合评估原始研究以确定干预措施的有效性。通过收集和总结当前证据并提供客观建议，它们可以为决策者提供宝贵的帮助。但是，它们很耗时。估计需要18个月到3年才能完成一次审核。Persephone分析了来自著名农业研究来源的500,000种非结构化文本摘要，以90％的准确性确定最终由专家研究人员选择的研究子集。我们证明了机器学习模型在将证据提供给决策者的手中是无价的。