No abstract

Ventilation and dissolved oxygen in Lake Superior are key factors that determine the fate of various natural and anthropogenic inputs to the lake. We employ an idealized age tracer and biogeochemical tracers in a realistically configured numerical model of Lake Superior to characterize its ventilation and dissolved O 2 cycle. Our results indicate that Lake Superior is preferentially ventilated over rough bathymetry and that spring overturning following a very cold winter does not completely ventilate the lake interior. While this is unexpected for a dimictic lake, no part of the lake remains isolated from the atmosphere for more than 300 days. Our results also show that Lake Superior's oxygen cycle is dominated by solubility changes; as a result, the expected relationship between biological consumption of dissolved O 2 and ventilation age does not manifest.

No abstract

Biological systems often have a narrow temperature range of operation, which require highly accurate spatially resolved temperature measurements, often near ±0.1K. However, many temperature sensors cannot meet both accuracy and spatial distribution requirements, often because their accuracy is limited by data fitting and temperature reconstruction models. Machine learning algorithms have the potential to meet this need, but their usage in generating spatial distributions of temperature is severely lacking in the literature. This work presents the first instance of using neural networks to process fluorescent images to map the spatial distribution of temperature. Three standard network architectures were investigated using non-spatially resolved fluorescent thermometry (simply-connected feed-forward network) or during image or pixel identification (U-net and convolutional neural network, CNN). Simulated fluorescent images based on experimental data were generated based on known temperature distributions where Gaussian white noise with a standard deviation of ±0.1 K was added. The poor results from these standard networks motivated the creation of what is termed a moving CNN, with an RMSE error of ±0.23 K, where the elements of the matrix represent the neighboring pixels. Finally, the performance of this MCNN is investigated when trained and applied to three distinctive temperature distributions characteristic within microfluidic devices, where the fluorescent image is simulated at either three or five different wavelengths. The results demonstrate that having a minimum of 103.5 data points per temperature and the broadest range of temperatures during training provides temperature predictions nearest to the true temperatures of the images, with a minimum RMSE of ±0.15 K. When compared to traditional curve fitting techniques, this work demonstrates that greater accuracy when spatially mapping temperature from fluorescent images can be achieved when using convolutional neural networks.