AI 中文总结
研究针对时空建模缺乏标准协议的问题，提出STeMP协议，通过标准化报告助于理解模型功能并在建模过程中提供指导，涵盖概述、模型和预测三部分，配套R包及网络应用，可辅助填写协议并返回警告，鼓励社区参与。
AI 中文摘要
时空机器学习建模是环境研究中的重要工具。机器学习模型对训练数据特征（如分布）和方法选择（如交叉验证策略）高度敏感。考虑到基于机器学习的环境地图在科学及实践中的重要作用，透明报告时空模型至关重要，但目前缺乏相关协议。本文提出STeMP协议，它有三个部分：概述、模型和预测。概述含元数据，模型和预测部分详细描述预测器、评估和软件等。协议定义托管在GitHub上并配有R包，可手动或半自动填写协议，遇到常见问题会返回警告，鼓励社区贡献和反馈。
英文摘要
Spatio-temporal machine-learning modelling is an important tool in environmental research. However, machine-learning models are highly sensitive to both the characteristics of the training data, such as its distribution, and methodological choices, including the cross-validation strategy. Each decision has impact and implications on the model itself as well as the estimation of the model quality and applicability for certain purposes. Taking into account the large role of machine-learning based maps of the environment in science and their transfer into practice, transparent reporting of spatio-temporal models, ideally using standardized model protocols, is essential to enable trust, transparency and comparability. However, such protocols are currently lacking for spatio-temporal modelling. We propose STeMP (Spatio-Temporal Modelling Protocol) to fill this gap by serving two purposes: standardized reporting to understand the model functioning as well as providing guidance during the modelling process by pointing at critical decisions and parameters. The protocol is structured in three sections: Overview, Model and Prediction. The Overview section contains metadata, while the Model and Prediction sections go into detail, describing predictors, evaluation and software, and further relevant elements of the modelling workflow. The protocol definition is hosted on GitHub and accompanied by an R-package (https://github.com/LOEK-RS/STeMP). The R-package contains a web application that can be used to fill the protocol either manually or in a semi-automated way from provided modelling objects. Warnings are returned from the protocol when common pitfalls are encountered, which may help authors as a guide through the modelling process but also support reviewers in the assessment of modelling studies. Via GitHub, incorporation of contributions and feedback from the community is encouraged.
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