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The analyses in this paper are based on datasets held by the British Geological Survey.
These measures are evaluated using benchmarks that are based on datasets formed by words pairs evaluated by experts, such as RG65 [35], MC30 [29] and AG203 [3].
It was also manifested in a higher frequency of failure of the mixed models to converge in scenarios 4 to 6 than in scenarios 1 to 3. We recall that both sets of scenarios are based on datasets with the exact same sample size and outliers.
But a comparison of scenarios 1 to 3 with 4 to 6, all of which are based on datasets with the same sample size but different genetic variances, makes it apparent that reducing the genetic variance markedly reduces predictive accuracy, even in the absence of outliers.
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The analysis was based on datasets from 16 gauged catchments in the Lower Mekong Basin, Southeast Asia.
These models were based on datasets of plot-scale Et, measured within five years post-fire, and published literature on post-fire changes in vegetation structure.
Previous studies that aimed at testing these models with empirical data were based on datasets extracted directly from the reading of the vagaries of the raw fossil record, without correcting for common fossil record biases (preservation and sampling).
Moreover, the paper to which Mr. Harris links approvingly is based on datasets that strive to be representative.
Our analysis is based on datasets from the systems that collated records of livestock movements (SAMS and CTS).
Unlike some older models for AAA repair, 20 22 all three models were based on datasets that included exclusively AAA repairs (both open repairs and EVAR).
Model development was based on datasets from standard tolerance tests (glucose and insulin) and a published clinical-trial dataset by El-Khatib et al. The respective simulation results are presented in the following.
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