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Non-stochastic model correctly classifies more than 93%and95%5% of chemicals in both training and external prediction groups, respectively.

In training series the model correctly classifies 79 out of 80 (specificity = 98.75%) set1 and 226 out of 238 (Sensitivity = 94.96%) set2 trajectories.

With an appropriate classification cutoff value, the predictive model correctly classifies 70% of drained lakes and 90% of undrained lakes, for an overall accuracy of 88%.

In independent validation series the model correctly classifies 26 out of 26 (specificity = 100%) set1 and 75 out of 78 (sensitivity = 96.15%) set2 trajectories.

The true identities of the samples are given in the figure legend and the results indicate that the PLS-DA model correctly classifies each spectrum with 100% accuracy.

However, the classification table indicates that the model correctly classifies 70.3% of the investigations.

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Similar(51)

Of the 37 breeding colonies of the test data set, the model correctly classified 31 (83.78%), the same percentage as the correctly classified absences (i.e. 31 out of 37).

The model correctly classified 83.7% of 132 mps and 98.89% of 274 control sequences in training.

The DA model correctly classified 58% of the selection outcomes in mussels and 57% in oysters.

The model correctly classified 88.47% and 92.75% of active and inactive compounds respectively, in training set.

The model correctly classify 98.462%% of distressed and 44.615 % of non-distressed firms.

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