The number of LC3A‐positive SLS was counted in the entire section at a magnification of 400×, and then, the number in each section was divided by the total number of fields included in the entire section to correct for different section sizes (Sivridis et al., 2013).
As was seen in Section 5.1, it was optimal to correct for the distortion in terms of that too few workers did choose to educate themselves.
As discussed in section 2.3, it is simple to correct simulation results for the missing influence of these atoms.
For this reason, the relationship between C I and ARI is examined in this section, and we attempt to correct C I using ARI.
Although we use and report the uncorrected Platt scores here simply as a means to show enrichment for targets we provide, in our Methods section, the calculations necessary to correct these scores to the genomic scale.
In Section 3, we develop a framework to correct for interference in network construction, a post-processing step that clusters regulators and calculates regulator interference within clusters for each target.
In the following sections, attempts are made to correct the Swarm ion drift measurements by determining and removing offsets.
As for H ybrid, since the relation classification model did not use the spreading rule (described in Section 2.6), it would only attempt to correct the entities that have co-occurring species words in the same sentence, affecting 36.04% entities, and therefore only marginally improved the performance over ML.
In this section, the proposed Bayesian method to correct for measurement error is illustrated using data from the study of Chan et al. [ 12].
Stereology avoids the need to correct for variation in section thickness and cell size of each case as established in the Abercrombie principle [ 28, 32], with confident, unbiased live counting of neurons at high magnification using 63 or 100× objectives.
