Abstract In this paper, we investigate different bias factor methodologies, including the standard bias factor method, the generalized bias factor method, and the product of exponentiated experimental values bias factor method. One new method is proposed—the representativity weighted bias factor method, which is based on weighted statistical dispersion. Finally, an old method, the extended bias factor method, is recast and reformulated in order to provide accurate results and new quantities to assess the usefulness of the integral experiments. These methods have been implemented and tested on a practical application targeting the Keff of a typical FTR (Fast Test Reactor) using up to four integral experiments. Results are compared against those of an adjustment taken as a reference using the same set of integral experiments. Based on results, we make several recommendations, including to adopt the extended bias factor method because it reproduces the results of the adjustment. In selecting experiments, the most important requirement is to have a large representativity factor, followed by an attached low experimental uncertainty. If two experiments are highly correlated, only one needs to be included. If an experiment has a very high representativity factor (e.g. 0.95), there is no need to include more experiments in determining the bias factor and the associated uncertainty reduction, and it is crucial to use experiments that capture the physics of the integral parameter under consideration.

中文翻译：

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重新审视用于验证快堆试验的偏置因子方法

摘要 在本文中，我们研究了不同的偏差因子方法，包括标准偏差因子法、广义偏差因子法和指数实验值乘积偏差因子法。提出了一种新方法——代表性加权偏差因子法，它基于加权统计离散度。最后，一种旧方法，即扩展偏置因子方法，被重新制定和重新制定，以提供准确的结果和新的数量来评估积分实验的有用性。这些方法已在针对典型 FTR（快速测试反应器）的 Keff 的实际应用中实施和测试，最多使用四个积分实验。结果与使用相同的积分实验组作为参考的调整的结果进行比较。根据结果​​，我们提出了几项建议，包括采用扩展偏差因子方法，因为它再现了调整的结果。在选择实验时，最重要的要求是具有大的代表性因子，其次是附加的低实验不确定性。如果两个实验高度相关，则只需要包括一个。如果实验具有非常高的代表性因子（例如 0.95），则无需包括更多实验来确定偏差因子和相关的不确定性降低，并且使用能够捕捉所考虑的积分参数物理特性的实验至关重要. 最重要的要求是具有大的代表性因子，其次是附加的低实验不确定性。如果两个实验高度相关，则只需要包括一个。如果实验具有非常高的代表性因子（例如 0.95），则无需包括更多实验来确定偏差因子和相关的不确定性降低，并且使用能够捕捉所考虑的积分参数物理特性的实验至关重要. 最重要的要求是具有大的代表性因子，其次是附加的低实验不确定性。如果两个实验高度相关，则只需要包括一个。如果实验具有非常高的代表性因子（例如 0.95），则无需包括更多实验来确定偏差因子和相关的不确定性降低，并且使用能够捕捉所考虑的积分参数物理特性的实验至关重要.