concentration estimation · monte-carlo under constraints
an ingredient list is ordered, not quantified.
brands publish the order, not the amounts. the engine estimates every concentration anyway — an llm prior, disciplined by label order, a cutoff marker and mass balance, then pushed through monte-carlo for honest error bars. below: the real analysis of a real sunscreen.
ingredients estimated
Σ posterior means
90%ci on every estimate
one formula, under the lens
select an ingredient · ↑ ↓ work toothe label, in order
INCI position · posterior mean shown as bar
llm prior
%
point estimate, pre-constraints
posterior mean
%
monte-carlo, all rules applied
90% interval
%
what the engine will defend
illustrative replay
the rulebook — every draw must obey all four
rule · label order
order is rank
rule · cutoff
the <1% boundary
rule · mass balance
it must sum to 100
rule · type priors
a sunscreen is not a serum
draws are bounded by typical use ranges for
sunscreen_spf · lotion formulations, per ingredient role.
range numerics are not part of this data harvest — stated, not shown
the bridge into scoring
risk scale factor
multiplies this ingredient's six risk scores at this concentration
benefit scale factor
multiplies its eight benefit scores at this concentration
risk −5‥+5 · benefit 0‥+5 · concentration-adjusted
the whole formula, reconstructed
45 real estimates · log scale · hover any column
posterior mean
90% interval
llm prior
cutoff
above the cutoff, means fall monotonically with position — the order constraint at work