Quantifying Uncertainty in Ecotoxicological Risk Assessment: MUST, a Modular Uncertainty Scoring Tool
Quantifying Uncertainty in Ecotoxicological Risk Assessment: MUST, a Modular Uncertainty Scoring Tool
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DOI：
10.1021/acs.est.0c02224
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发表时间：
2020-10-06
影响因子：
11.4
通讯作者：
Raderman, Will
中科院分区：
文献类型：
--
作者：
Kostal, Jakub;Plugge, Hans;Raderman, Will
无论是进行风险、危害还是替代评估，人们总是要努力将多个毒理学阈值调和成一个单一的结果。当结合来自许多不同来源的多个证据时，考虑数据不确定性的作用是很重要的。不确定性是所有科学数据所固有的。然而，在毒理学评估中，争议和不确定性通常被低估；它们缺乏方法上的透明度；或者他们不能很好地整合定性和定量的信息来源。同样，在模型开发中，数据管理很少得到足够严格的执行，特别是在应用大数据统计时。为了克服必须协调不同数据的决策过程的障碍，我们开发了一种不确定性评分工具，可以训练它来重现特定的决策范例，并确保从业者在复杂场景中的判断的一致性。虽然该工具旨在帮助生态毒理学评估和预测模型开发，但它的适用性扩展到任何需要综合不一致数据的决策过程。在这里，我们强调了开发过程，并在几个典型的生态毒理学案例研究中展示了该方法的实用性。
Whether conducting a risk, hazard, or alternatives assessment, one invariably struggles with the task of reconciling multiple available values of toxicological thresholds into a single outcome. When combining multiple pieces of evidence from many different sources, it is important to consider the role of data uncertainty. Uncertainty is inherent to all scientific data. However, in toxicological assessments, controversies and uncertainties are typically understated; they lack methodological transparency; or they poorly integrate qualitative and quantitative sources of information. Similarly, in model development, data curation is rarely performed with sufficient rigor, particularly when applying big data statistics. To overcome the hurdles of a decision process that must reconcile divergent data, we developed an uncertainty scoring tool that can be trained to reproduce specific decision-making paradigms and ensure consistency in the practitioner's judgment across complex scenarios. While designed to aid with ecotoxicological assessments and predictive model development, the tool's applicability extends to any decision-making process that calls for synthesis of incongruent data. Here, we highlight the development process, as well as demonstrate the method's utility in several prototypical ecotoxicological case studies.