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An optical fiber cutback measurement estimates distributed optical fiber attenuation by comparing a long guide with the short input portion left after cutting the output end back. Keep the launch untouched. For the following original functional model, fix wavelength, source output, input alignment and collected output cone; subtract a dark reading and keep the photodetector in its linear range. Renewed end-face quality and changed normal mode populations can still spoil the comparison. The diagram describes a measurement principle, not Kao's historical short-glass apparatus.

Let $L_{\ell }>L_s$ be lengths in kilometres, $P_{\ell },P_s$ the corresponding positive dark-subtracted readings, and $C$ the same launch and detection factor. A uniform guide obeysHere ${\alpha }_{\mathrm{dB}}$ and its estimate are numerical values in $dB/km$. Dividing eliminates $C$; taking a logarithm proves the formula. A changed $C$ instead adds an error $10{\log }_{10}(C_s/C_{\ell })/(L_{\ell }-L_s)$. Reconnecting the launch between readings defeats this cancellation.

For original chosen values $L_s=0.1\mathrm{km}$, $L_{\ell }=2.1\mathrm{km}$, $P_s=100\mu \mathrm{W}$ and ${\alpha }_{\mathrm{dB}}=0.5dB/km$, the predicted long reading is $P_{\ell }=10010^{-0.1}\simeq 79.4328\mu \mathrm{W}$. The ratio gives $1\mathrm{dB}$ for the removed $2\mathrm{km}$ and hence $0.5dB/km$.

Suppose an unremoved additive detector background contributes $b=5\mu \mathrm{W}$ to both readings. Then the apparent coefficient isThis is a bias of an estimator: a common additive background pushes the ratio toward one, whereas a common multiplicative factor cancels. For independent small zero-mean errors with standard deviations ${\sigma }_s,{\sigma }_{\ell }$ on the dark-subtracted readings, a first-order Taylor expansion givesChoosing both standard deviations as $1\mu \mathrm{W}$ gives ${\sigma }_{\hat{\alpha }}\simeq 0.03491dB/km$. A shared estimated dark offset introduces covariance, so it requires a different uncertainty calculation. Near zero signal, the logarithm and this linear approximation become unreliable.