 Repeatability floor from surface-area model discrepancy

A repeatability floor from surface-area model discrepancy is the remaining uncertainty when repeated measurements reduce input noise but reuse the same imperfect anatomical predictor. Repeating height and mass measurements does not independently redraw the relationship between those quantities and one person's actual body surface area.

An original local model writes prediction $X_i=S+D+{ϵ}_i$, where $S$ is the true area, $D$ a shared model discrepancy and ${ϵ}_i$ independent repeat noise. Represent uncertainty about $D$ by variance ${\tau }^2$ and repeat noise by variance ${\sigma }^2$, assuming independence between them. For the mean of $n$ repeats,The correlation is a consequence of reusing the same discrepancy. The GUM requires significant common-input correlations to be included and permits additional model quantities for incomplete knowledge.^[ref]

For stipulated $\sigma =0.04{\mathrm{m}}^2$ and $\tau =0.08{\mathrm{m}}^2$, the mean's standard uncertainty is about $0.0894{\mathrm{m}}^2$ after one reading, $0.0825{\mathrm{m}}^2$ after four and $0.0801{\mathrm{m}}^2$ after one hundred. The lower limit is $0.08{\mathrm{m}}^2$ in this model, not zero. These values illustrate the distinction rather than report a measured error distribution for the Mosteller formula.

Repeat-count targets under shared model error make a useful planning calculation. Requiring the repeat-noise variance to be at most a fraction $\eta$ of the shared variance gives $n\ge {\sigma }^2/(\eta {\tau }^2)$. With the chosen values and $\eta =0.01$, 25 repeats suffice; further repetition changes the total uncertainty little while leaving the dominant model term intact.

The shared term need not fluctuate between actual readings. For a fixed person, an unknown prediction bias can remain fixed while repeatability looks excellent. Its distribution represents uncertainty or variation across the validation population, not a physical mechanism that changes body shape on each measurement.

A useful validation therefore combines repeat measurements with independent reference-area evidence across an appropriate range of individuals. Distinguish within-person repeatability, shared calibration effects and model residuals. If a systematic bias is established, correcting it is a separate step from describing residual uncertainty.