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* Wilson outliers: look through the list of reflections labeled as "aliens" in [[CORRECT.LP]]. Decide whether they follow a slowly decaying non-Wilson distribution (resulting in many reflections with Z > 8 instead of almost none in the case of a Wilson distribution), or whether the top ones are true outliers. The latter occurs most often from ice reflections (these may even be there when no ice rings are visible). <br /> My personal rule of thumb is that when the integer parts of Z ("int(Z)") are the numbers 8, 9, ... n, but there are no aliens (or just a single one) with int(Z) = n+1, then I consider all aliens with Z > n+1 as outliers. A different rule of thumb would be to simply consider aliens with Z of 20 or more as outliers. <br /> Outliers should be put (i.e. copied) into REMOVE.HKL, and [[CORRECT]] then should be re-run.<br /> It is useful to inspect the list of aliens after re-running CORRECT; maybe a few more aliens should be put into REMOVE.HKL. But this process of rejecting Wilson outliers usually converges very very quickly.
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* Wilson outliers: look through the list of reflections labeled as "aliens" in [[CORRECT.LP]]. Decide whether they follow a slowly decaying non-Wilson distribution (resulting in many reflections with Z > 8 instead of almost none in the case of a Wilson distribution), or whether the top ones are true outliers. The latter occurs most often from ice reflections (these may even be there when no ice rings are visible). <br /> My personal rule of thumb is that when the integer parts of Z ("int(Z)") are the numbers 8, 9, ... n, but there are no aliens (or just a single one) with int(Z) = n+1, then I consider all aliens with Z > n+1 as outliers. A different rule of thumb would be to simply consider aliens with Z of 20 or more as outliers. <br /> Outliers should be put (i.e. copied) into REMOVE.HKL, and [[CORRECT]] then should be re-run.<br /> It is useful to inspect the list of aliens after re-running CORRECT; maybe a few more of those should be put into REMOVE.HKL. But this process of rejecting Wilson outliers usually converges very quickly.
 
* Another way to judge Wilson outliers is to identify resolution ranges that deviate from 1. in the table '''HIGHER ORDER MOMENTS OF WILSON DISTRIBUTION OF ACENTRIC DATA''' in [[CORRECT.LP]]. "Aliens" that are put into REMOVE.HKL will lower the values in these resolution ranges!
 
* Another way to judge Wilson outliers is to identify resolution ranges that deviate from 1. in the table '''HIGHER ORDER MOMENTS OF WILSON DISTRIBUTION OF ACENTRIC DATA''' in [[CORRECT.LP]]. "Aliens" that are put into REMOVE.HKL will lower the values in these resolution ranges!
 
* SCALEPACK users: don't confuse this process of rejecting Wilson outliers with the iterative procedure of rejecting scaling outliers that is usually done when using SCALEPACK. Scaling outliers are handled non-iteratively in [[XDS]]; the only way to influence [[XDS]] in this respect is by modifying [[WFAC1]].
 
* SCALEPACK users: don't confuse this process of rejecting Wilson outliers with the iterative procedure of rejecting scaling outliers that is usually done when using SCALEPACK. Scaling outliers are handled non-iteratively in [[XDS]]; the only way to influence [[XDS]] in this respect is by modifying [[WFAC1]].
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