Add options for machine learning on the oceanographic profiles to help determine fit boundaries (depth), best fit orders (P, T, C), and methods based on real-world cases.
For example, should the center of a gyre be fit in the same way as an estuary? If not, what does the machine think it should do?
In addition, it would be interesting to further explore basic grouping algorithms of oceanographic bottle data (as functions of T, S, and depth). This was done on OSNAP in 2022, but wasn't explored thoroughly.
Add options for machine learning on the oceanographic profiles to help determine fit boundaries (depth), best fit orders (P, T, C), and methods based on real-world cases.
For example, should the center of a gyre be fit in the same way as an estuary? If not, what does the machine think it should do?
In addition, it would be interesting to further explore basic grouping algorithms of oceanographic bottle data (as functions of T, S, and depth). This was done on OSNAP in 2022, but wasn't explored thoroughly.