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Using a design-based, stratified random sampling framework we found that 68%95%5% confidence interval (CI) [53 to 84%]) of the detected post-fire logging was correctly classified, such that both fire and clearcutting disturbances were positively verified by the reference data.
This indicates that 85% of the reference dataset was identified by the extracted drain data, that 85% of the extracted drains were matched by the reference data and 71% of the matched drains contributed to the entire extraction and reference drain network.
Preferred names proposed by the reference data resources were used at low frequency: 19.3% for medical terms, 33% for protein/gene names and 46.3% for chemical entities.
Thus, the simulation of static loading with our system was validated by the reference data in the literature, suggesting that our FE model of osteocyte-lacunar-canalicular is reliable.
However a baseline accuracy that is driven by the reference data set size and the overall population effective population size enables gBLUP to estimate a breeding value for unrelated animals within a population (breed), using information previously ignored by pedigree based BLUP methods.
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The collected data is used to train artificial neural networks (ANNs) which would improve the accelerometer outputs by estimating the reference data from the actual sensor outputs.
Such work can be made possible by replacing the reference data set containing whole genes of rice with other data sets.
When the two acyl moieties of these molecules were searched for by referring to the reference data, 38 5ePE and 36:5ePE were found to be primarily derived from 18 0p/20 4 PE and 16:0PE20:4 PE, respectively.
The sample data was analyzed by subtraction of the reference data, normalization to the protein concentration and DSC cell volume, and interpolation of a cubic baseline.
Data were analysed by subtraction of the reference data for a sample containing only buffer, using the Origin 7 software.
While the pattern of prediction accuracies across included PC showed that PCR had a higher potential than GREML, the model that was selected by CV within the reference data could not capitalize on this potential.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com