Rigor and falsifiability
Why it is hard to dismiss
A new theory should not merely tell a beautiful story. It must reduce fitting freedom, expose its assumptions, provide tests, and state where it could fail.
No fitted continuous parameter
Values are not obtained by observable-by-observable regression. Remaining choices must be discrete, named, and justified.
Same logic across domains
Gaps, observables, chemical periods, IE, EA, time, and geometry are presented as readings of one persistence principle.
Explicit status labels
The site separates identity, theorem, derivation, bridge, validation, prediction, and open problem.
Reproducible scripts
Important numerical results are linked to companion scripts or computable pages.
Falsifiable predictions
The theory exposes validation windows and breaking points instead of remaining purely interpretive.
What strengthens the case
PT’s current strength is that it does not rest on a single isolated success. It proposes a general constraint, then shows consequences in very different registers. The more the same mechanisms recur without free parameters, the more expensive coincidence becomes.
The right posture is not to “believe” PT, but to test whether its discrete commitments, derivations, and measurements keep fitting together as precision increases.
What makes it testable
An overly flexible theory can always adapt. PT must remain rigid: every correction needs an identifiable geometric, arithmetic, or informational origin.
This is why the limits page and the status table are as important as the results pages. They prevent proof, validation, and working hypothesis from being confused.