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Existing strategies for QTL mapping were built on testing for a direct association between genotype and end-point phenotype.
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Traffic flow was estimated from videos and, mapping was built among videos and vehicle detector data.
More than just a beautiful historical object, the map provided the foundation upon which subsequent geological mapping was built.
The success of the mapping was built upon an automated system which transforms Landsat images into standardized time-series metrics.
Until recently Google's maps were built into Apple's iPad and iPhone as well as into smartphones that use Google's own Android operating system.
Characterization maps were built using data from both techniques.
Thirty learner maps were built by 30 subjects.
Wear maps were built to highlight the correlation between friction coefficient and wear mechanisms.
Concentration distribution maps were built after pixel-to-pixel predictions and their characteristics were analyzed.
In [8], depth maps were built by using only MVs extracted from the compressed video.
Based on quasi-similarity transformation theory, pole maps were built up as unified geometric identification between given positions and guidance mechanisms.
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