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.
Positive ensemble mean with a negative realization
A two-outcome probability model for an illustrative entropy-valued random variable; it is not a specified physical fluctuation law or reported observation.
Example
Let with weights and . Both weights are nonnegative and sum to , so is a finite probability space as defined in Finite probability spaces, outcome weights, events, and event probabilities. For every event , write for its event probability. Let be given by and , where and the values are numerical values in the unit . By Real random variables on finite probability spaces and their finite distributions, is a finite-valued real random variable on this finite probability space, so its expectation is a finite sum. The label is only an illustrative entropy-valued variable; this example adopts no physical path law.
This arithmetic example illustrates the distinction in Hypotheses for microscopic fluctuation claims.
Facts & Assumptions
Given: The two-outcome model and assignments stated above.
Expectation is the sum of each attained value times its probability: for a real random variable on a finite probability space, .
Finite probability spaces, outcome weights, events, and event probabilities: nonnegative weights summing to define a finite probability space, and each event probability is the sum of its outcome weights.
Real random variables on finite probability spaces and their finite distributions: every function from a finite probability space to is a finite-valued real random variable.
The assignments are , , , , and .
Verification
Given: [F1]–[F3] and [A1].
The two values of are distinct because . By [F2]–[F3], is a real random variable on the stated finite probability space, so [F1] gives . Its unit is .
The event is exactly , so by [F2] its probability is .
Steps 1.1 and 1.2 exhibit a positive mean together with a negative realization of nonzero probability. Therefore positivity of an ensemble mean alone does not imply a nonnegative value in every trial. The example is not a fluctuation theorem or a claim about measured entropy production.