Two journal-level indicators, respectively the mean ($m^i$) and the standard deviation ($v^i$) are proposed to be the core indicators of each journal and we show that quite several other indicators can be calculated from those two core indicators, assuming that yearly citation counts of papers in each journal follows more or less a log-normal distribution. Those other journal-level indicators include journal h index, journal one-by-one-sample comparison citation success index $S_j^i$, journal multiple-sample $K^i-K^j$ comparison success rate $S_{j,K^j}^{i,K^i }$, and minimum representative sizes $\kappa_j^i$ and $\kappa_i^j$, the average ranking of all papers in a journal in a set of journals($R^t$). We find that those indicators are consistent with those calculated directly using the raw citation data ($C^i=\{c_1^i,c_2^i,\dots,c_{N^i}^i \},\forall i$) of journals. In addition to its theoretical significance, the ability to estimate other indicators from core indicators has practical implications. This feature enables individuals who lack access to raw citation count data to utilize other indicators by simply using core indicators, which are typically easily accessible.

翻译：我们提出将两个期刊层面指标，即均值（$m^i$）和标准差（$v^i$），作为每个期刊的核心指标，并证明在假设各期刊论文的年引文数量大致服从对数正态分布的前提下，可由这两个核心指标推导出若干其他指标。这些其他期刊层面指标包括：期刊h指数、期刊单篇比较引文成功指数 $S_j^i$、期刊多样本 $K^i-K^j$ 比较成功率 $S_{j,K^j}^{i,K^i }$、最小代表性样本量 $\kappa_j^i$ 和 $\kappa_i^j$，以及期刊论文在期刊集合中的平均排名（$R^t$）。我们发现，这些指标与直接使用期刊原始引文数据（$C^i=\{c_1^i,c_2^i,\dots,c_{N^i}^i \},\forall i$）计算的结果具有一致性。除理论意义外，通过核心指标估算其他指标的能力具有实际应用价值——该特性使无法获取原始引文数据的用户，仅需使用通常易于获取的核心指标即可利用其他指标。