Sentence examples for more robust sampling from inspiring English sources

While providing a useful basis for national wood assessments, Ferguson (2014b) has identified some limitations in plantation forecasting in Australia, including the need for more robust sampling for growth and yield and information on replanting assumptions.

More robust sampling over a larger number of collection sites would help in confirming this trend.

Further investigation of the 'seven centers' hypothesis, will require the development and application of more variable markers to a much more robust sampling of the available safflower germplasm.

When a single heritability estimate based on a more robust sampling of data was used to estimate realized accuracies, results became more stable in the analyses using smaller subsets of the data [See Additional file 2: Figure S1B] and assessments of DGV accuracy could then be based solely on the estimated correlation between phenotype and predicted DGV within the validation set.

A more robust sample of African-American voters comes from the Gallup poll, which interviews about 3,500 people a week for its national tracking poll and does include cellphones in its sample.

As recommendations for new research agendas, it is suggested that future studies use an experimental design with a more robust sample, more equally stratified in relation to gender, and cover the different levels of education, income, and professional categories.

He speculated that they were only able to find trace amounts of EV-D68 in some patients because fluid samples were taken from the children about one week after the onset of respiratory symptoms, which may have been too long to wait for more robust samples.

In the following section we describe a potentially more robust sample size approach.

These two years of data were collected independently of one another but combined to provide a more robust sample size for the current analysis.

As such, an interim sample size analysis will also be conducted at n = 50 allowing for an acceptable estimate of the population standard deviation and hence considerably more robust sample size calculation to ensure adequate power.

Harris et al. [ 14] presented the Genetic Learning Across Datasets concept (GLAD), a new Semi-Supervised Learning (SSL) method for combining independent annotated datasets and unannotated datasets with the aim of identifying more robust sample classifiers.