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Results from hierarchical linear analysis models identified a significant effect for privatization while controlling for a major alternative explanation.
Our results demonstrated that both random forest and linear unmixing models identified a distribution of rainforest and eucalypt stands that was comparable to existing ecological vegetation class (EVC) maps based primarily on manual interpretation of high resolution aerial imagery.
The STLDLA and STLD models identified a QTL at 74.1 cM (Tables 1 and 2).
Neither single-pollutant nor multipollutant models identified a significant effect of SO2 on OM.
The M8 models identified a total of eight sites as being under positive selection with posterior probabilities >0.99 (Table 3).
The K and KP CML models identified a total of 321 markers (Additional file 6) significantly associated with resistance to stem canker (p-value < 0.05).
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Extrapolation of habitat models identified an abundance of habitat suitable for burrows.
Individual songbird models identified an apparent 40% threshold in sagebrush land-cover, over which songbird abundances nearly doubled.
Univariable negative binomial regression models identified an association between risk of IMD and increasing weekly influenza A activity the week of, and two weeks prior to, case occurrence, and RSV activity during the week of case occurrence only (Table 3).
In another study, proportional hazard models identified an optimal set of 50 prognostic mRNA transcripts using a 5-fold cross-validation procedure.
Logistic regression models identified an elevated admission serum cTnI as the only statistically significant variable for the long term composite outcome, MACE.
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