What does this equation mean?

No sealed trial of the kind proposed later in this article has been run, and every number in this section is invented to show what the construction predicts, not what it has found. Suppose a population of N=30 models: ten fine-tuned directly on a seeded source’s outputs ( $\mathcal{M}_V$ ), ten exposed only to a scaffold template and a handful of the source’s outputs recirculated through an unrelated synthetic-data pipeline ( $\mathcal{M}_H$ ), and ten trained on the same task distribution with no contact with the source at all ( $\mathcal{M}_\varnothing$ ), each tested against a k=200 -probe battery for one seeded, low-prior trait. Suppose $I_0$=0.03 , a 3 percent spontaneous rate, and $\bar I_V$=0.61 , $\bar I_H$=0.22 . Then $T_V$=$\frac{0.61-0.03}{1-0.03}$$\approx$0.598 and $T_H$=$\frac{0.22-0.03}{1-0.03}$$\approx$0.196 : on this invented population, vertical exposure would explain roughly 60 percent of the gap between baseline and observed incidence, horizontal exposure roughly 20 percent — smaller, as one would expect from a noisier, more indirect channel, but not zero.