## 5 Results and Discussion  
The system was initially applied to data collected at the CSIRO Wharf in Hobart between 25 August and 5 November 2010. This node was composed of two EC-250 temperature and conductivity sensors fixed to the wharf, one at a depth of 1.0 m below chart datum and the other at a depth of 9.5 m below chart datum (chart datum was at the level of the lowest possible astronomical low tide). The sensors were field calibrated in early September 2010, following their deployment on 25 August 2010. The automated data QA/QC procedure described in Section IV was applied to this dataset with the constant \(k\) assigned a value of 0.8. A subset of this data was also processed manually to generate error bars for comparison with the automated system. The subset was picked to represent data collected throughout the deployment and included all data collected on 26 to 28 August, 12 to 13 September, 26 to 27 September, 10 to 11 October and 25 to 26 October; a total of 2,672 datapoints from each sensor. Figures 4 and 5 compare the manually calculated error bars and the error bars generated automatically for two samples from the shallow temperature dataset, one early in the deployment and the other later in the deployment. Prior to the temperature spike in Figure 4, the manually calculated and automatically generated error bars are close to identical. This is because this early in the deployment the main contribution to error is the accuracy of the sensor itself. The fuzzy set system indicated an overall small uncertainty; through Equation (3) this results in a calculated error bar equal to the sensor accuracy. During and immediately following the spike, the fuzzy set system slightly overestimates the error associated with \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Small** & **Medium** & **Large** \\ \hline Temperature (\({}^{\circ}\)C) & 0.1 & 0.3 & 1.2 \\ Conductivity (\(\mu\)S/cm) & 200 & 1,000 & 8,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Empirically determined coefficients used in Equation (3). Figure 4: Shallow water temperature with manually calculated and automatically generated error bars for a period early in the sensor deployment. the measurement. This is a conservative approach and can be corrected later when the data is inspected by a domain expert. Later in the deployment, the measurement errors are larger (note the greater range of temperature on the y-axes in Figure 5). In this case, the fuzzy set system performs well prior to the jump in temperature. During the jump it conservatively overestimates the error. Immediately following the jump it slightly underestimates the error, but is still within a factor of two of the manually calculated error bars. The automatically generated error bars were expressed as a percentage of the manually-determined error bars over the 2,672 datapoints for each of the four sensors. Table 3 presents the distribution of these calculated percentages. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Automatically generated** & **Temperature** & **Temperature** & **Conductivity** & **Conductivity** \\
**error bar/Manually** & **(–1 m)** & **(–9.5 m)** & **(–1 m)** & **(–9.5 m)** \\ \hline Above 300\% & 13 (0.5\%) & 1 (0.0\%) & 547 (20.5\%) & 537 (20.1\%) \\ Between 200 and 300\% & 166 (6.2\%) & 339 (12.7\%) & 268 (10.0\%) & 365 (13.7\%) \\ Between 150 and 200\% & 119 (4.5\%) & 84 (3.1\%) & 336 (12.6\%) & 342 (12.8\%) \\ Between 66.7 and 150\% & 2,131 (79.8\%) & 2,224 (83.2\%) & 980 (36.6\%) & 993 (37.2\%) \\ Between 50 and 66.7\% & 108 (4.0\%) & 9 (0.3\%) & 99 (3.7\%) & 138 (5.2\%) \\ Between 33.3 and 50\% & 92 (3.4\%) & 7 (0.3\%) & 177 (6.6\%) & 140 (5.2\%) \\ Below 33.3\% & 43 (1.6\%) & 8 (0.3\%) & 265 (9.9\%) & 157 (5.9\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ratio of automatically generated to manually determined error bars (expressed as a percentage) for eleven days chosen to be representative of the sensor deployment period.