The per-image resolution estimate was the 5th percentile of the spot d spacings -
an extreme order statistic, so it measured where detection stops rather than how
well the crystal diffracts. A large cell puts more reflections past the same
threshold and scored better than a small cell that diffracts further; intensity
was not used at all, so a weak crystal padded with spurious high-resolution
detections ran away; and nothing clamped the answer to what the detector can
deliver. Against the resolution the merged data actually reach it was 42% out in
log-RMS, with 1 of 38 rotation datasets inside 0.2 A.
Take instead the 1/d^2 beyond which 30% of the sum of sqrt(I) over the image's
non-ice spots lies, report 1/(2.25 sqrt of it), clamp at the detector corner, and
take the median over images. A quantile from the middle of the distribution
measures the shape of the falloff - the crystal's own exp(-B/2d^2) - where an
extreme one measures the threshold. sqrt(I) is the Poisson significance of a
summed photon count, so a marginal high-resolution detection cannot carry the
answer and neither can a handful of very strong low-resolution reflections. The
2.25 is the multiplicity gain: merging keeps measuring intensities a fixed factor
in 1/d past the point where a single frame detects them.
Spearman 0.881 -> 0.954, log-RMS 42% -> 8.9%, median error 0.79 -> 0.07 A, and 32
of 38 within 0.2 A. Both constants sit on a broad plateau, the scale is stable
across dataset halves and across resolution ranges, and no second predictor
survives leave-one-out. The residual is around 9%, set by multiplicity, symmetry
and radiation damage - none of which a spot list can see.
The estimate feeds only reporting: the image stream, HDF5, the plots, the scan
result and the preview ring. It sets no cutoff and no search limit, and the
scaling and merging output is byte-identical.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_016NNnL26LAvruQ9eLUUWvrJ
leonarski_fandClaude Opus 5
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