Over the past decade alongside increased focus on computational reproducibility significant efforts have been made to define reproducibility. However, these definitions provide a textual description rather than a framework. The community has sought conceptual frameworks that identify all factors that must be controlled and described for credible computational reproducibility. The PRIMAD model was initially introduced to address inconsistencies in terminology surrounding computational reproducibility by outlining six key factors: P (Platforms), R (Research objective), I (Implementations), M (Methods), A (Actors), and D (Data). Subsequently various studies across different fields adopted the model and proposed extensions. However, these contributions remain fragmented and require systematic integration and cross-disciplinary validation. To bridge this gap and recognising that PRIMAD provides a broadly applicable framework for reproducibility in computational science, this work undertakes a focused investigation of the PRIMAD model. It combines the models previous extensions into a unified framework suitable for diverse research contexts. The result is PRIMAD-LID, a discipline-diagnostic reproducibility framework that retains the original six PRIMAD dimensions and enhances each with three overarching modifiers: Lifespan (temporal qualifier), Interpretation (contextual reasoning) and Depth (necessary granularity), thereby establishing a more cohesive and robust foundation for computational reproducibility practices.

翻译：过去十年间，随着对计算可重复性关注的日益增加，学界已付出显著努力来定义可重复性。然而，这些定义仅提供了文本描述而非框架。研究界一直寻求能够识别所有必须控制和描述的因素以建立可信计算可重复性的概念框架。PRIMAD模型最初被提出，旨在通过概述六个关键因素来解决计算可重复性术语不一致的问题：P（平台）、R（研究目标）、I（实现）、M（方法）、A（执行者）和D（数据）。随后，不同领域的多项研究采纳了该模型并提出了扩展。然而，这些贡献仍然零散，需要系统性的整合与跨学科验证。为弥合这一差距，并认识到PRIMAD为计算科学中的可重复性提供了一个广泛适用的框架，本研究对PRIMAD模型进行了聚焦性探讨。它将模型先前的扩展整合为一个适用于多样化研究场景的统一框架。其成果是PRIMAD-LID，一个具备学科诊断能力的可重复性框架。该框架保留了原始的六个PRIMAD维度，并通过三个全局修饰符对每个维度进行了增强：生命周期（时间限定）、解释（上下文推理）和深度（必要粒度），从而为计算可重复性实践建立了一个更具凝聚力和鲁棒性的基础。