New protocol standardizes spatio-temporal machine-learning reporting

Researchers have introduced STeMP, a Spatio-Temporal Modelling Protocol designed to standardize the reporting and guidance of machine-learning models used in environmental research. This protocol aims to enhance trust, transparency, and comparability by detailing critical methodological choices and data characteristics. STeMP is hosted on GitHub and includes an R-package with a web application to assist users in filling out the protocol, offering warnings for common pitfalls and supporting reviewers in assessing studies. AI

IMPACT Standardizes reporting for environmental ML models, improving transparency and comparability in scientific research.

RANK_REASON The item describes a new protocol for spatio-temporal machine-learning modeling, published on arXiv, which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• alphaXiv
• arXiv
• CatalyzeX Code Finder for Papers
• Connected Papers
• DagsHub
• GitHub
• Gotit.pub
• Hugging Face
• Influence Flower
• Litmaps
• R package
• ScienceCast
• scite Smart Citations
• STeMP

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →