Datasheets for Machine Learning Sensors

Machine learning (ML) sensors offer a new paradigm for sensing that enables intelligence at the edge while empowering end-users with greater control of their data. As these ML sensors play a crucial role in the development of intelligent devices, clear documentation of their specifications, functionalities, and limitations is pivotal. This paper introduces a standard datasheet template for ML sensors and discusses its essential components including: the system's hardware, ML model and dataset attributes, end-to-end performance metrics, and environmental impact. We provide an example datasheet for our own ML sensor and discuss each section in detail. We highlight how these datasheets can facilitate better understanding and utilization of sensor data in ML applications, and we provide objective measures upon which system performance can be evaluated and compared. Together, ML sensors and their datasheets provide greater privacy, security, transparency, explainability, auditability, and user-friendliness for ML-enabled embedded systems. We conclude by emphasizing the need for standardization of datasheets across the broader ML community to ensure the responsible and effective use of sensor data

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The influence of geomorphology on large wood dynamics in a low gradient headwater stream

Understanding large wood dynamics is critical for a range of disciplines including flood risk management, ecology and geomorphology. Despite the importance of wood in rivers, our understanding of the mobility of large wood remains limited. In this study individual pieces of large wood were tagged and surveyed over a 32 month period within a third and fourth order lowland forest river. Individual piecesof wood were found to be highly mobile, with 75% of pieces moving during the survey period, and a maximum transport distance of 5.6 km. Multivariate analyses of data from this study and two other published studies identified dimensionless wood length as the important factor in explaining likelihood of movement. A length threshold of 2.5 channel widths is identified for near functional immobility, with few pieces above this size moving. In addition, for this study, wood type, branching complexity, location and dimensionless wood diameter were found to be important in determining mobility only for sinuous reaches with readily inundated floodplains. Where logjams persist over multiple years they were shown to be reworked, withcomponent pieces being transported away and replaced by newly trapped pieces. The findings of this study have implications for river management and restoration. The high mobility observed in this study demonstrates that only very large pieces of wood of length greater than 2.5 channel widths should be considered functionally immobile. For pieces of wood of length less than the channel width the possibility of high rates of mobility and long transport distances should be anticipated