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The experimental data were predicted with the Cubic-Plus-Association equation of state (CPA EoS).
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As a result, the experimental mass transfer coefficient data was predicted with a high degree of accuracy.
The learner node takes numerical data as input and via predictor node the test data is predicted with a score and class.
Laminar flow data not only from this work but also from work from the literature is very well predicted while transitional and turbulent flow data are predicted with less accuracy, requiring improvements on predicting transition points.
These data were predicted in accordance with the corresponding data available in the literature.
The SNPs in A. glycines B1 454 data were predicted by gsMapper program (Roche) with an arbitrary criterion of at least 4 reads supporting the consensus or variant.
A total of 366 data points were predicted with a mean deviation of 8.1%.
Individual data points were predicted with crossed fixed and random effects.
Genes in simulated reads and reads of the FAMeS data set were predicted with Genemark heuristic version 1.1, MetaGene and MetaGeneAnnotator as provided at http://metagene.cb.k.u-tokyo.ac.jp/metagene on February 1st 2009, respectively, and Orphelia as provided at http://orphelia.gobics.de/download.jsp on May 1st 2009.
The model was validated against fault-free experimental data showing a reasonable level of agreement for both the start-up and cycling regimes: the system energy consumption was found to be within ±10% agreement with the experimental data, while the compartment air temperatures were predicted with a maximum deviation of ±1 °C.
This means that any training data being predicted by the current model with an accuracy of up to ε can be neglected.
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CEO of Professional Science Editing for Scientists @ prosciediting.com