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The 2002 experiment trapped a $6.3\mu \mathrm{m}$ bead in water with a $980\mathrm{nm}$ laser, a $100\times$, numerical aperture $1.3$ objective and a piezo-driven microscope stage. A quadrant photodiode tracked bead position at $1\mathrm{kHz}$ with approximately $500\mathrm{Hz}$ bandwidth and reported $15\mathrm{nm}$ resolution. Stage translation drove water past the fixed optical trap at $1.25\mu m/s$. Weak trapping prolonged the transient; stationary recording before translation established the position reference. Stokes drag law calibration was checked against the bead's measured diffusion coefficient. ^[ref]

From calibrated displacement and calibrated harmonic optical trap force, the experimenters evaluated the dimensionless driven-bead dissipation functionalAmong 540 trajectories, negative values became less frequent with duration. Figure 2 compared the negative/positive count ratio with $⟨e^{-{\Sigma }_t^{\mathrm{drive}}}{⟩}_{{\Sigma }_t^{\mathrm{drive}}>0}$; its agreement tested the paper's integrated transient relation. The detailed log-ratio plot in Figure 4 used simulated particle sample trajectories, not bead measurements. Near-zero sign discrimination and stage-start transients limited the earliest points. ^[ref]

For any continuous probability distribution satisfying the same-law relation, integration over $s>0$ yieldsThis conditional relation explains the comparison without equating it to the unconditional integral fluctuation theorem. The experiment supports a specified driven-system relation; it does not establish a universal same-law symmetry for any definition of heat or entropy production. In particular, a transient work-like functional should not silently acquire the actual-distribution endpoint term in finite trajectory entropy production. Negative realizations are compatible with positive mean dissipation and the Second law of thermodynamics; they do not provide a cyclic machine with reliable work output from a single thermal reservoir.

Integrating the Crooks fluctuation theorem over work and using normalization of the reverse probability distribution givesConsequently Jensen's inequality gives $⟨W{⟩}_F\ge \Delta F$. Some realizations may nevertheless have $W<\Delta F$. This is a work relation with equilibrium endpoint preparations, while the integral fluctuation theorem for total entropy production uses the actual final forward distribution. ^[ref]

A steady state does not by itself give every fluctuating observable an entropy production symmetry. If the dimensionless total entropy production ${\Sigma }_t$ has the same-law finite relation and a large deviation principle with rate function $I$ for ${\Sigma }_t/t$, then, on its common domain,The first expression denotes leading exponential scaling; the second follows by taking the scaled logarithm of the probability ratio. A finite-state, autonomous Markov process with time-even states, positive bidirectional rates and a positive stationary distribution supplies a standard setting for a finite total-entropy production relation. The stochastic system entropy is then bounded, so its endpoint contribution does not change the long-time rate function of bath entropy production. ^[ref]

Unbounded state spaces can have endpoint fluctuations on the scale relevant to a large deviation principle. Dropping system entropy, changing the measured observable to heat, or changing the reverse preparation therefore requires a fresh argument. The exact finite integral fluctuation theorem, a same-law finite relation, and an asymptotic rate function symmetry are distinct claims.