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Self-rated health was recoded in two classifications (excellent or very good, good to poor).
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The final range of motion was used to the determine functional outcome according to the Judet classification: Excellent More than 100°; Good 80° 99°; Acceptable 50° 79°; Poor Less than 50°.
The interobserver agreement of the classification of the ratio images could be an issue; however, in our classification, excellent agreement was observed, with high kappa values.
For FHR signal baseline estimation and classification, excellent results are obtained in the application of artificial neural networks, besides the obvious limitation of the training set size and constitution.
These classifications have excellent intra- and inter-observer agreement for liver fibrosis (kappa values >0.80) [22] 22].
The classification analysis shows that supervised machine learning methods are promising candidates for handling with success the DNA Barcode species classification problem, obtaining excellent classification performances.
According to the most frequently used guidelines for interpretation of kappa agreement (Landis and Koch 1977), the intra- and inter-rater reliability and accuracy of the Müller classification were excellent when considering three signs of the classification and substantial when four signs were considered.
The use of multifractal features in bread crumb texture classification showed excellent performance.
Kappa value of 1 indicates perfect agreement, >0.75 good classification, >0.80 excellent agreement and 0.50 a performance not better than chance.
Table 2 Individual component classification Groundwater quality categories I II III IV V Value for Fi 0 1 3 6 10 Table 3 Standards of the groundwater quality classification Level Excellent Good Moderate Poor Very poor F < 0.80 0.80~< 2.50 2.50~< 4.25 4.25~< 7.20 > 7.20.
Our MS-MLPA based 3R classification showed excellent correlation to mRNA and protein expression.
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