Start with the denominators, because in this write-up they carry the argument. Ninety-one rejections across 139 verdicts leaves 48 verdicts that ended in ratification [7][1], and 91 divided by 139 is 65.5% [2]. A ratified verdict closes a finding. A rejected one buys the finding exactly one re-proposal with the feedback attached [9]. That makes the accounting closed enough to solve: if F findings reached a decision and k of them were ratified on the second pass, the two published counts force 2F + k = 187 [3].
Set the ratification share at exactly half and you need F = 96, which would require 96 rejections, and there were 91 [4]. The nearest fits that respect both counts are 93 findings with a single successful re-proposal, at a 52% ratification share, or 88 findings with 11 successful ones at 55% [5]. So between roughly 2% and 22% of re-proposals survive the second look [5]. The feedback loop is doing very little rescuing.
The adoption cost falls out of the same numbers. 139 verdicts implies 139 proposals and 139 reviews, so 278 model calls against at most 93 findings, about three calls per finding before anybody receives a ticket [6]. That is the sticker price of challenging every decision before it becomes state [2].
The severity split needs more care than the post gives it. 24 critical against 24 high is 50%, not the 55% reported [17][7]; the ratified pair, 22 against 32, does come out at 41% [18][8]. The subtotals also do not reconcile with the top line, 48 and 54 against 91 rejections and 48 ratifications [9]. The direction is probably right, but I would want the export before quoting the percentage.
What the reviewer actually catches is narrow, which is why it catches anything. Both quoted rejections compare a proposed severity against evidence the proposal itself cited: an escalation to critical from a CVSS base of 7.8 [13], and a critical rating set against a scanner CVSS of 5.4 and an NVD description at high, 7.8 [14]. That is text checked against text. For the 65% to transfer to your pipeline, proposals have to carry their evidence inline, the reviewer has to see the same evidence, and the rubric has to make escalation past the citation a rejectable condition [3][4]. A reviewer grading judgment with no citations to check would emit a rate that means nothing.
The reviewer's own accuracy sits outside the measurement. The author's conclusion that the triage model inflates severity rests on the reviewer's stated reasons and on that split [16][17][18], and no human severity call is scored against either model. Read strictly, 65% is consistent with a proposer that inflates and with a reviewer that is merely strict.
The craft worth copying is the correction. An earlier version of the piece read one silent cycle as restraint; the honest version is that both workers woke at 09:01 UTC on 28 August, on cycle 30693, with an empty acceptance sweep and an empty set of SLA clocks [21][22]. Actual deliberation shows up elsewhere, in cycle 9003's wait=9, nine findings evaluated and nine left alone because their deadlines had not arrived [23]. The duplicate-delivery guard had to be provoked by hand, since Pub/Sub delivers at least once and production had declined to oblige [25].
