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First, such metrics are sensitive to problems related to the nonuniform distribution of proteins over GO terms due to the equal weight given to all terms.
Results indicate that landscape metrics are sensitive to sub-pixel values and can vary greatly with fractional cover.
The results indicate that landscape metrics are sensitive to the variation of pixel values of fractional ISA, and the integration of LST, LSMA.
Given the many differences in experimental design between these studies and ours, it remains to be seen under what conditions recognition metrics are sensitive to sleep-mediated consolidation processes.
This study is the first application of DKI on the effects of midazolam and WD exposure, and the findings demonstrate that diffusion metrics are sensitive indicators of changes in the complexity of neurite architecture.
The results demonstrate that most of the metrics are sensitive to certain pattern scenarios, yet are not sensitive to others; therefore, none of them is appropriate for all aspects of a landscape pattern.
Similar(50)
Soil health metrics were sensitive to long-term tillage practices.
However, alternative RRF metrics were sensitive to the number of days used in the calculation of the RRF.
The landscape metrics were sensitive to changes in soil surface micro-morphology especially after the 1st erosive rain event, indicating significant erosional feature development between the initial wetting and first erosive rainfall.
Both metrics were sensitive to pixel density, with smaller particles yielding larger rounding errors due to the lower number of pixels available.
Additionally, several studies (Kearns et al. 2005; Saura and Martinez-Millan 2001) warn against using pattern metrics that are sensitive to spatial extent (i.e., those that vary in relation to size of the watershed under study), as these metrics are not good discriminators of landscape structure between catchments that vary in size.
Related(18)
criteria are sensitive
indicators are sensitive
arrangements are sensitive
attributes are sensitive
factors are sensitive
measures are sensitive
parameters are sensitive
calculations are sensitive
characteristics are sensitive
metrics are experimental
metrics are imperfect
metrics are straightforward
metrics are outstanding
metrics are wonderful
metrics are optimized
metrics are open
metrics are local
metrics are detailed
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