Email: Hi ____, {sentence about how I know you}. I'm a data science educator and one day I realized that I might have a lot to learn from a personal trainer (beyond the obvious of how to exercise). If you could spare 20 minutes of your time, I'd love to talk to you more about some challenges I think we might have in common such as: How you remind clients that their goals are attainable What you do to be more approachable and less intimidating to beginners Your approach to correcting "bad form" without demotivating clients Let me know if you'd be interested in chatting and I can send you some available times.

Matt "plugged in" rounding to correct precision--only applied to new data (since the last month or so) Part of azmetr could be checking for correct precision Matt has a place online where we can edit the measured variables and it will propogate to derived variables (we think). Split tasks: modeling and workflow automation Phase one is alert Jeremy of extreme values and imputed values (a daily report table published with Quarto, for example) Could fit multivariate model as additional step (can't be raining all day and have high solar radiation) Could detect things in derived variables that we don't see in measured variables beause of transformations? Report refinements Report currently uses all data for rule-based validations and just one day for forecast-based validations. This doesn't make sense. Need some flexibility in terms of what days are being viewed in the report and consistency between types of validations.

The transect that Kristina sampled contains: Southern Coastal Plain: https://bplant.org/region/134 longleaf pine flatwoods and savannas richer forests with slash pine, pond pine, pond cypres, american sweetgum, southern magnolia, laurel oak, white oak, american beech (i.e. mix of evergreen, evergreen hardwood, and deciduous hardwood PFTs) Floodplains with bald cypres, pond cypres, water tupelo, sweetgum, green ash, water hickory ("temperate.Hydric" PFT??) Florida scrub monoculture pine plantations

NOTE: I never got this to work. The docker containers don't run for reasons I don't understand. There are only 2 possibilities currently for ED versions because there is only one release tag---v.2.2.0. So its either that or the development version "git". If you want a specific commit, you'll have to edit the code in the Dockerfile to git checkout a specific SHA. Why would you want to do this? When running ED2 models on the HPC, you might want to: use the development version of ED2 when the container on docker hub isn't updated use a version of PEcAn.ED2 other than the develop branch or the latest release (e.g. a PR you're working on) use some custom combination of PEcAn and ED2 versions

What's the deal with the posteriors.pdf plots for setariaWT? Vcmax: Prior in BETY says dist = lnorm, a = 3.75, b = 0.3, n = 12 query.trait.data() also shows 3.75, 0.3, 12 Prior in Vcmax.model.bug says beta.o ~ dlnorm (3.75, 11.1111111111111)#BBB. This is just because BUGS paramaterizes distributions differentlly None of those are truncated at ~20 like in the plot above Output of query.trait.data():