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Message:

> Hi Micha,
> 
> Thank you very much for taking the time to answer in such detail – the relative-scale criterion for choosing σ (small relative to the other observation uncertainties, rather than an absolute epsilon) is exactly the kind of practical guidance I was missing, and the danger-circle analogy makes the geometric-stability point very clear.
> 
> One follow-up, if you don't mind: the relative criterion is straightforward when comparing observations of the same type (e.g. distance vs. distance, as in your example). In my case the network mixes angles/directions (radians) with distances (meters) and, on top, a gyro azimuth that I want to treat as "almost fixed." Is there a sensible way to judge whether a given σ is "small enough" across observation types with different units/physical dimensions – for example, by comparing standardized residuals or weight contributions after a first adjustment run, rather than the raw σ values themselves? Or would you simply convert everything to a common measure (e.g. via the resulting positional effect) before comparing?
> 
> Attached two pics for better understanding the network.
> 
> Thanks again for your help!
> 
> [img]https://i.imgur.com/5IXI5aS.png[/img]
> [img]https://i.imgur.com/u1MZOcZ.png[/img]

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