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Author decision: replacement for the stacking step-error metric (blocks P1) #50

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@mdenolle

Author decision needed; blocks the step-error item in #39 (S-RE.3, SCI-11).

The stacking figure's step-error metric is the minimum over 120 post-event epochs minus the pre-event median, minus the same for the truth. Two consequences the review found: a negative value means the recovered drop is deeper than the true drop (over-estimated), the opposite of how both drafts read it; and at short stacks the minimum over noisy epochs is biased negative by the extreme-value effect, so the -5.4 percent at 1-day stacks is a property of the metric, not of the step. Choose the replacement:

  • post-window median minus pre-window median (robust, simple, slightly smooths the step);
  • a fitted step (amplitude of a Heaviside plus exponential recovery fitted over the post-event window; matches the earthquake truth's form);
  • keep the minimum but report it as such with its extreme-value bias stated.

The chosen metric goes in the Fig 9 caption and Section 3.6 is rewritten from the regenerated sidecar.

Activity

  1. added this to the Iteration 3 revision milestone on Sep 11, 2026
  2. added
    P1science corrections
    needs-authordecision or input only the author can supply
    review-iter2from the iteration-2 pre-submission review
    on Sep 11, 2026
  3. mdenolle commented on Sep 13, 2026

    @mdenolle
    MemberAuthor

    P1 (PR #52) proceeds under option 1 as the default: the step amplitude is the median of the 120 days after the event minus the median of the 120 days before, on the recovered series and on the truth alike (synthetic_demo.step_amplitude, also used by the one-at-a-time and multiverse drop metrics). Consequence worth knowing before you decide: with this definition the stack length has no first-order effect on the step in the multiverse (a stack shorter than the 120-day windows barely moves the medians), whereas the withdrawn minimum-based metric had ranked it first. A fitted step (option 2) would recover sensitivity to smearing at the cost of a model assumption. Switching is a one-function change.

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