Sentence examples for maps were determined from inspiring English sources

Accuracy assessments of maps were determined using error matrix and Kappa statistic (Congalton and Green 1999).

From regions of interest representing a vesicular structure and cytosol, the excitation and emission intensity maps from the excitation-emission-scan along with the excitation and emission spectra from the intensity maxima of the maps were determined.

Activation maps were determined for both pairs of tasks in eleven of the twelve subjects.

The EstC population was genotyped with 45 established SSLP markers [21], [47] and 179 SNP markers [32] Genetic linkage maps were determined using Joinmap 3 [48]; the marker orders agreed with the published Col-0 sequence.

For each BOLD signal change peak the corresponding macro-anatomically defined brain region, the probabilities of belonging to the currently available micro-anatomically defined brain areas, and the maximum-probability-map [15] based peak assignment to the probabilistic-anatomical maps were determined.

RF maps were determined by correlating the neural response (1 for white checks, −1 for black checks) to the checkerboard stimuli (16 × 16 checks).

Collinearity between maps was determined from common mapped unigenes by the assignment of rank order to the markers in each map.

Verified by field samples and aerial photographs, the accuracy of the resulting habitat unit maps was determined to be 91.8% overall in the 1997 TM classification and 89.5% overall in the 1975 MSS classification.

The optimal number of these template maps is determined by a modified cross-validation criterion [78], [79], [80], [83].

The genetic positions of these markers were compared and the co-linearity of the maps was determined.

Enrichment of Metacore maps was determined by the p-value of hypergeometric distribution representing probability that a transcript would be matched to a map by chance.