Sentence examples for predictions of data from inspiring English sources

For the three cultivars, the goodness of fit of predictions of data from 2011 was satisfactory both during banana growth and post-harvest ripening.

However, model validation during banana growth was not satisfactory in any of the three cultivars, as revealed by the RMSEs and RRMSEs of predictions of data from 2012, whose values ranged between 3.67 and 5.60 mmol Kg FW−1, and between 0.40 and 0.74 respectively.

Model validation during banana post-harvest ripening for the three cultivars was satisfactory, as revealed by the RMSEs and RRMSEs of predictions of data from 2012, whose values ranged between 6.55 and 10.54 mmol Kg FW−1, and between 0.24 and 0.29 respectively.

Alternatively, the results of these computations are used as source data to enhance the predictions of data-centric models.

Sivapalan et al. (2003) presented three methods for predictions of data-sparse basin responses: (1) interpolation or extrapolation of response information from gauged to ungauged basins, (2) measurements by remote sensing (e.g. radar, satellites)., (3) application of process-based hydrological models to reduce the dependency on specific precipitation inputs.

A prediction of data that are not yet received as input can be accomplished, provided the data are what will later be defined to constitute a stationary time series and provided the prediction is restricted according to a well-defined optimization procedure.

Figures 26, 27, 28, and 29 show the results of the prediction of data traffic load for a particular day based on the training of the data traffic load of the day before (Experiment C2). Figure 5 Training of previous half-year's data traffic load for the prediction of the following half-year's data traffic rates (enlarged).

Put simply, if two parameters affect the prediction of data in a similar way, their unique estimation will be less efficient.

While we would not normally expect such a dramatic increase in data yield, based on our prediction of data loss for two initially biased sequences (Fig. 3A), we were able to fully rescue an otherwise completely failed Illumina run using bareback processing.

Prediction of data using OFS 222) of a small population of arteries is illustrated in Figure 5.

As a result, prediction of data points at the extremes of the output would not be optimal.