One probe, two very different answers
Bayes on canary extraction. A surface multiplies your odds by e / fp — huge, because a chance match (fp) is ~2−64. Silence multiplies them by (1−e) / (1−fp) — about 1−e, which is ~1 when a frontier model suppresses (emission rate e small). Move the sliders; the math is live and client-side.
The asymmetry is log-scale, not rhetoric
Why a positive is loud
A 128-bit canary's 64-bit withheld half has a chance-match rate fp ≈ 2⁻⁶⁴. So even a small emission rate makes BF+ = e/fp astronomically large — a single surface is worth ~60 bits and pins the posterior at ~1. There is almost nothing to explain away.
Why silence is nearly mute
Silence is worth BF- = (1−e)/(1−fp) ≈ 1−e. When suppression makes e small, that's ~1 — you multiply your odds by one and learn nothing. The gauge won't call a negative "clean," because the reading hangs entirely on a suppression rate you can't measure — the slider you can break.
What it is — and is not
Sound
Plain Bayes, no fit. python selftest.py proves with no network: a positive → posterior ~1 (~60 bits); under suppression, silence stays within 0.02 of the prior (<0.1 bit); the asymmetry is hundreds of times in bits; and silence becomes informative only as e rises — decisive non-ingestion at e=0.99. The page runs that exact math client-side.
Limits
It cannot make a negative mean more than it does — that is the point. The emission rate is unknown and unmeasurable for a closed model; the gauge shows the whole answer as a function of it rather than pretending to a number. It quantifies belief; it does not detect anything. For detection, see the boundary-crossing instruments.
silence.py (Bayes factors, posterior, bits, gauge) · selftest.py (the four claims, no network). Companion to the boundary-crossing detectors: it weighs the silence they can return.