Sentence examples for classification of drivers from inspiring English sources

Or are they dependent on the classification of drivers as independent contractors, which, if challenged, could cause their valuations to come crashing down?

The Teamsters union and several drivers for the ride-hailing company Lyft are charging that the $12.25m settlement reached in a class-action lawsuit over the employment classification of drivers is not good enough.

Uber has also faced a similar tribunal decision related to its classification of drivers as self-employed, and is continuing to appeal.

Uber has also faced a similar tribunal decision related to its classification of drivers as self-employed, and is continuing to appeal.

For this reason, their outcome is considered as less uncertain, once leading drivers have played out. Figure 6 Classification of drivers based on level of importance and uncertainty.

The GMB Union notes that in Uber London's latest accounts, released last week, it warns shareholders that it faces "numerous legal and regulatory risks", both pertaining to existing regulations and the development of new regulations, as well as as a result of "claims and litigation" related to its classification of drivers as independent contractors.

Classification of driver genes.

The paper entitled "nonparametric single-trial EEG feature extraction and classification of driver's cognitive responses" by Chin-Teng Lin et al. investigates the use of electroencephalographic (EEG) signal analysis for classification of the driver's cognitive responses to traffic lights.

In addition, it allows the classification of driver genes as TSGs or OGs.

A cut-point of 0.56 provided the highest correct classification rate of drivers and resulted in the following figures: (i) Sensitivity = 50%, (ii) Specificity = 97.92%, (iii) positive predictive value = 80%, negative predictive value = 92.16%, correct classification = 91.07%.

A cut-point of 0.4 provided the highest correct classification rate of drivers and resulted in the following figures: (i) Sensitivity = 62.5%, (ii) Specificity = 97.92%, (iii) positive predictive value = 83.33%, negative predictive value = 94%, correct classification = 92.86% (Table 6).