摘要翻译：
通过先进的检测，可以避免复杂自然系统中灾难性的制度转移。最近的工作提供了一个原理证明，许多接近灾难性转变的系统可以通过早期预警指标的透镜来识别，例如方差增加或返回时间增加。尽管人们普遍认识到这种预测所涉及的困难和不确定性，但所提出的方法几乎没有描述其预期的误差率。如果没有复制、控制或后知后觉的好处，这些方法的应用必须量化不同指标在避免错误警报方面的可靠性，以及它们对遗漏细微警告信号的敏感性。我们提出了一种基于模型的方法，以便量化可靠性和敏感性之间的这种权衡，并允许不同指标之间的比较。我们表明，即使在有利的假设下，这些错误率对于普通指标来说也是相当严重的，并且还说明了基于模型的指标是如何提高这种性能的。我们演示了一个预警指标在不同数据集中的表现是如何变化的，并建议不确定性量化成为预警预测的一个更核心的部分。
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英文标题：
《Quantifying Limits to Detection of Early Warning for Critical
  Transitions》
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作者：
Carl Boettiger and Alan Hastings
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最新提交年份：
2012
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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一级分类：Physics        物理学
二级分类：Data Analysis, Statistics and Probability        数据分析、统计与概率
分类描述：Methods, software and hardware for physics data analysis: data processing and storage; measurement methodology; statistical and mathematical aspects such as parametrization and uncertainties.
物理数据分析的方法、软硬件：数据处理与存储；测量方法；统计和数学方面，如参数化和不确定性。
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一级分类：Quantitative Biology        数量生物学
二级分类：Populations and Evolution        种群与进化
分类描述：Population dynamics, spatio-temporal and epidemiological models, dynamic speciation, co-evolution, biodiversity, foodwebs, aging; molecular evolution and phylogeny; directed evolution; origin of life
种群动力学；时空和流行病学模型；动态物种形成；协同进化；生物多样性；食物网；老龄化；分子进化和系统发育；定向进化；生命起源
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英文摘要：
  Catastrophic regime shifts in complex natural systems may be averted through advanced detection. Recent work has provided a proof-of-principle that many systems approaching a catastrophic transition may be identified through the lens of early warning indicators such as rising variance or increased return times. Despite widespread appreciation of the difficulties and uncertainty involved in such forecasts, proposed methods hardly ever characterize their expected error rates. Without the benefits of replicates, controls, or hindsight, applications of these approaches must quantify how reliable different indicators are in avoiding false alarms, and how sensitive they are to missing subtle warning signs. We propose a model based approach in order to quantify this trade-off between reliability and sensitivity and allow comparisons between different indicators. We show these error rates can be quite severe for common indicators even under favorable assumptions, and also illustrate how a model-based indicator can improve this performance. We demonstrate how the performance of an early warning indicator varies in different data sets, and suggest that uncertainty quantification become a more central part of early warning predictions.
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PDF链接：
https://arxiv.org/pdf/1204.6231