1. Methods for automatic processing
Requirements
Accurate enough
Useful for doing significant science
Accepted by majority of community
Robust
Useful on most of the solar disk
Errors limited
Fast enough
Real time
Post facto
Others?

2. Considerations No manual intervention by definition
Excludes AZAM?
Semel: “locations with poor solutions are interesting”
Test auto methods against gold standard
Is AZAM the gold standard?
Any auto method will leave some discontinuities (some may be real, some due to noise or algorithm problems)

3. Considerations
May not need to disambiguate every image in a time series (but, flare changes)
Photospheric magnetic field is intermittent, not continuous, so prefer a method that minimizes dependence on continuity

4. Meeting the requirements
Only c2 and c3 seem promising enough (pending noise sensitivity tests)
Need to accelerate these by a large factor (but seems possible by coding in C)
Is there hope of improving other methods?
Is there some new approach that is better?

5. Personal opinion
The more good additional information that can be added to the c2/3 error metrics, the better (e.g. height gradient of |B| or div BHorizontal)
A hybrid approach may be helpful (one method for network and another for active regions)
Are we fooling ourselves with non-linear spatial averaging of real solar fields?
Should work harder on getting physically useful information that does not depend upon disambiguation