Extended RAM Legacy Stock Assessment Database version 4.61

Extended RAM Legacy Stock Assessment Database version 4.61

Please be aware that this ‘mdl’ ('model fits included') version 4.61 of the RAM Legacy Stock Assessment Database (RAMLDB) additionally includes data not contained in the assessments themselves, but generated from assessment data. Generated data are used to fill in gaps where possible, for example, if stock assessments do not include reference point estimates. Supplemented data include calculations from assessment values, conversions of biomass variables, and reference points estimated post-hoc with surplus production models. The ‘Documents’ folder includes documentation files on the methods used for these calculations, conversions and model fitting. When assessment data are available, they are used preferentially in all cases.

Although we identify data that were supplemented from data that were derived from stock assessments, some collector variables in RAMLDB include both of these data sources (with assessment values preferred). We recommend that the average user does not use this 'mdl' version of RAMLDB because of the additional assumptions and calculations involved. Instead, to avoid confusion between assessment-derived values and supplemented values, we recommend using the ‘asmt’ ('assessment data only') version of RAMLDB version 4.61. This 'asmt' version (10.5281/zenodo.7814638) is also available in our Zenodo repository: https://zenodo.org/communities/rlsadb/

扩展版RAM Legacy鱼类种群评估数据库（RAM Legacy Stock Assessment Database, RAMLDB）4.61版

请注意，本‘mdl’（含模型拟合结果）版RAM Legacy鱼类种群评估数据库（RAMLDB）4.61版，额外纳入了非直接源自种群评估报告、而是基于评估数据衍生生成的数据集。衍生数据用于填补评估报告中的数据空白，例如当种群评估未提供参考点估计值时。补充数据集涵盖基于评估值的计算结果、生物量变量转换结果，以及通过剩余产量模型事后估算得到的参考点。‘Documents’文件夹中包含了上述计算、转换及模型拟合方法的说明文档。但凡有可用的原始评估数据，将优先予以采用。

尽管我们已对补充数据集与种群评估衍生数据集进行了区分，但RAMLDB中的部分采集变量仍同时包含两类数据源（评估值优先级更高）。考虑到该‘mdl’版数据库需额外引入假设并开展大量计算，我们建议普通用户避免使用此版本。为避免评估衍生值与补充值之间产生混淆，推荐采用RAMLDB 4.61版的‘asmt’（仅含评估数据）版本。该‘asmt’版（10.5281/zenodo.7814638）同样可在我们的Zenodo社区仓库：https://zenodo.org/communities/rlsadb/ 中获取。