meandthewallaby
Joined 4 years ago
Joined 4 years ago
You can look this up for yourself: https://www.gridstatus.io/live/caiso
Incidentally, I agree with you. While there could be some editing for length (oh well), the point was well made with a great example to start out with, and a bit of a discussion about some of the effects of being trapped in the Ennui Engine. It definitely hit on something I've noticed about myself.
I have a pile of books I've been meaning to read but haven't gotten to. I have lots of articles that I'd like to read but haven't made time yet. But I'd pull up Reddit and just scroll there. I deleted Twitter when Elon bought it and decided to burn it to the ground, and I'll be deleting Reddit now. Not so much to make a stand, but really just using this opportunity of upheaval as a way for my old head to extricate itself from the Ennui Engine.
Maybe you have a state column that's derived that you cannot move to another step in the workflow until all nulls are filled in, but you've let the UI save what data it knows about and move on. It totally depends on what the user is doing and why we're skipping steps/data.
We should be very liberal with accepted inputs. I call them "Fuck It Buttons." There are lots of cases where you want a "Fuck It" button to just go around all the data entry and get an answer or move on with minimum info. Warn that the data isn't complete and we're using defaults, and don't just make output look the same as a complete workflow, but let them go through, nonetheless. Health care is just one example, but "Fuck It" comes up in every industry.
This is the UI/UX equivalent of knowing which hills to die on.
Again, you, as an end user, don't need to know any of that. The CRPS scorecard is a very specific measure of error. I don't expect them to reveal the technical details of the model, but an industry expert instantly knows what WeatherBench[1] is, the code it runs, the data it uses, and how that CRPS scorecard was generated.
By having better dispersed ensemble forecasts, we can more quickly address observation gaps that may be needed to better solidify certain patterns or outcomes, which will lead to more accurate deterministic forecasts (aka the ones you get on your phone). These are a piece of the puzzle, though, and not one that you will ever actually encounter as a layperson.
[1]: https://sites.research.google/gr/weatherbench/