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!