Based on the four community classifications, sampling across 11 Kebeles (villages) in the Aba'ala districts was made.
Table 4 Classification based on Cl−/HCO3− values (Revelle 1941) Cl−/HCO3− ratio Classification Sample numbers Total no. of samples Percentage <0.5 Not affected 9, 17, 19, 20 4 19.1 0.5–6.6 Slightly to moderately affected 1 8, 10 16, 18, 21 17 80.9 >6.6 Severely affected – – –.
Table 2 Classification based on water quality index WQI Classification Sample numbers Total no. of samples Percentage <50 Suitable 2 4, 7, 8, 10, 11, 20 8 38.2 50 80 Moderately polluted 1, 5, 6, 9, 18, 19 6 28.5 >80 Severely polluted 12 17, 21 7 33.3.
Fig. 6 Representative micro-FTIR absorption spectra of a group A, Cr-rich pyrope, and b subgroup B1, amphibole lamella type pyrope Table 4 Micro-FTIR analyses of pyrope from Garnet Ridge Chemistry Micro-FTIR analysis Classification Sample no.
According to the WHO classification, samples included normal brain, grade II astrocytoma, grade III oligodendroglioma, and GBM.
Among the records for which the classification rules and the intent-flag yielded the same classification (sample A), the classifiers performed very well, with sensitivity, specificity, positive and negative predict values all above 95%.
The new independent sample (sample B), similar to that used to generate the new classification (sample A), included 100 trio families (one RA patient and both parents) of French Caucasian origin (criteria fulfilled for each of the four grandparents).
In this section we show that classification with sampling outperforms standalone classifiers on the HCUP data set (table 8).
The classification of sampling occasions as increase, peak, and decline/low therefore appears to be appropriate (Table 1).
Even when post-classification sampling is undertaken, cost and accessibility constraints may result in imprecise estimates of map accuracy.
In addition to discussing maximum posterior probability estimators, this article reports on a simulation study comparing three approaches to estimating map accuracy: 1) post-classification sampling, 2) resampling the training sample via cross-validation, and 3) maximum posterior probability estimation.
