在某种程度的定义的下的,因果关系是模糊的。
Cause and effect below a certain level of definition is blurred.
所以理论上学习效应和规模效应不同,尽管实际操作中由于不确定性和模糊的因果关系可能很难区分它们。
Thus learning effects are theoretically distinct from scale effects, even if in practice they my be hard to differentiate due to e.g. uncertainty and causal ambiguity.
研究表明模糊因果图能有效地用于故障分析,比原来的因果图方法具有更大的灵活性和适应性。
The research shows that Fuzzy Causality Diagram is so effective in fault analysis, and it is more flexible and adaptive than conventional method.
Cause and effect below a certain level of definition is blurred.
在某种程度的定义的下的,因果关系是模糊的。
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