Sentence examples for correct identifications for from inspiring English sources

Exact(2)

In the simulation, the standard deviation of the Laplace distributions was set to 0.6 for all baseline conditions (which corresponded to an expected value of 71% correct identifications for participant P1, assuming that this participant's criteria were set midway between the mean stimulus positions along the decision axis).

For a quite complex, real-world metabolomics sample, such as an E. coli cell lysate, we find that this approach produces correct identifications for only a small subset the compounds that can be achieved by 2D NMR methods with many additional false positive identifications.

Similar(58)

Testing successfully returned a sequence and correct identification for all of the known rhino horn samples and vouchered rhino samples from museum and zoo collections, and provided species level identification for 47 out of 52 unknown samples from seizures.

Intuitively, size would seem to be an important object attribute since we often rely upon knowledge of object size for correct identification; for example, we learn to distinguish between a viola and a violin primarily by their relative size.

Correct identification for the 8 atypical isolates was achieved after partial sequencing of 16S rRNA gene leading to S. pneumoniae, 2 Inquilinus limosus, A. xylosoxidans, Serratia marcescens, S. aureus, Burkholderia multivorans, and P. aeruginosa (Table 1).

According to these patterns, the probabilities of correct identification for Strep.

The methods of correct identification for mycobacterial species in clinical laboratories have changed dramatically over the past two decades.

A similarity-based search in BactPepDB accepting a correct identification for a hit in the same species, and with over 90% identity led to the identification of 56 of them.

The median number of correct identification for all items increased significantly from the first to the second moment for Hydration with water (P = 0.001, n = 22); SRO hydration (P <0.001, n = 22) and Send to hospital (P = 0.001, n = 22).

Application of a Discriminant Function (DF) produced from a 25% random sample of the data set resulted in 99.9% and 99.6% correct identification for the blunt- and sharp-snouted lenok, respectively.

To examine whether the improvement seen in Fig.  2 was affected by where, in the trial sequence, the added loud stimulus appeared, we looked to see whether there was a difference in percent correct identification for the four baseline stimuli when they followed a baseline stimulus versus when they followed an added stimulus.

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