How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Maxwell speed marginal and Poisson number example
Conditional on ACω for the Euclidean Lebesgue product reference; exact d=3 massive ideal-gas canonical speed law and one grand-canonical activity choice for one operationally indistinguishable species, including the vacuum sector; no measured gas data.
Example
Assume as required by Exact canonical and grand ideal-gas preparations. In a canonical sector of the massive ideal-gas model of one operationally indistinguishable species, choose any mass in kg and inverse temperature in J. The most probable speed of a uniformly selected particle is and its ensemble mean speed is . In the separate grand model, which includes the vacuum sector, choose the chemical potential so the mean activity parameter equals , where is in m, in metres, and in joules. The particle count then has the Poisson law, including .
Facts & Assumptions
Given: , the massive ideal-gas canonical and grand preparations of Exact canonical and grand ideal-gas preparations for one operationally indistinguishable species, with , and the vacuum convention .
Exact canonical and grand ideal-gas preparations: in , the uniformly selected particle's speed mode and mean are and ; with the defined vacuum sector, the grand count has Poisson mass for every , where .
The real exponential function and the number by a power series, The natural logarithm as the inverse of the exponential function: and are inverse functions on and , so for .
Choose the grand model parameter ; this is a parameter selection, not an observation.
Verification
Specializing the clauses of [F1] gives the stated mode and mean of the model speed distribution; these are distributional functionals, not values assigned to every individual particle.
Substituting [A1] and applying [F2] gives , hence in [F1]. The Poisson formula then gives , so the and probabilities both equal .
Remarks
The displayed values are exact consequences of the specified distributions. They are not measured speeds or particle counts.