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To check if errors could cancel each other at stand level (i.e. no directional bias), tree biomass were summed for each monitor and the relative bias per monitor was computed as follow: bias_{j} left( % right) = frac{{mathop sum nolimits AG B_{ij - mathop sum nolimits AGB_{0} }}{{mathop sum nolimits AGB_{0} }},where i = the ith tree, j = the jth monitor and AGB0 = best tree biomass estimate.
The slope between PAEE and 1000 steps per day was calculated for each monitor using generalized linear models.
The absolute discrepancy for each monitor was determined as the median absolute value of ((steps detected – observed steps)/observed steps) ×100%.
We calculated a signed discrepancy score for each monitor to assess the relationships between monitor step counts and observed step counts, as well as step counts between the monitors.
We regressed the residuals for each monitor against local land use characteristics for each monitor and a smooth function of traffic density.
The daily standardized deviations for each monitor on each day were averaged, producing a daily averaged standardized deviation.
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Feasibility studies were then performed for each monitoring method.
The field data were collected on a monthly basis and the daily average was recorded for each monitored parameter.
In the Tables 3 and 4 the source's profiles for each monitoring station are shown as example.
Subsequently, the convex optimization technique is engaged to compute an appropriate quantity of physical servers for each monitoring window.
Average ± standard deviation of each triplicate was then used to express the relative expression level of ER+ and ERαKD cell lines for each monitored gene.
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