diff --git a/protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/tools/walkers/variantrecalibration/GaussianMixtureModel.java b/protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/tools/walkers/variantrecalibration/GaussianMixtureModel.java
index 17b3a63d7..2a9b2b5cc 100644
--- a/protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/tools/walkers/variantrecalibration/GaussianMixtureModel.java
+++ b/protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/tools/walkers/variantrecalibration/GaussianMixtureModel.java
@@ -320,5 +320,4 @@ public class GaussianMixtureModel {
     protected List<MultivariateGaussian> getModelGaussians() {return Collections.unmodifiableList(gaussians);}
 
     protected int getNumAnnotations() {return empiricalMu.length;}
-
 }
\ No newline at end of file
diff --git a/protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/tools/walkers/variantrecalibration/VariantRecalibrator.java b/protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/tools/walkers/variantrecalibration/VariantRecalibrator.java
index bb6f80835..b5145f76d 100644
--- a/protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/tools/walkers/variantrecalibration/VariantRecalibrator.java
+++ b/protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/tools/walkers/variantrecalibration/VariantRecalibrator.java
@@ -490,57 +490,46 @@ public class VariantRecalibrator extends RodWalker<ExpandingArrayList<VariantDat
                 dataManager.setData(reduceSum);
                 dataManager.normalizeData(); // Each data point is now (x - mean) / standard deviation
 
-                // Generate the positive model using the training data and evaluate each variant
                 final GaussianMixtureModel goodModel, badModel;
                 final List<VariantDatum> positiveTrainingData = dataManager.getTrainingData();
                 final List<VariantDatum> negativeTrainingData;
 
-                File inputFile = new File(inputModel);
+                final File inputFile = new File(inputModel);
                 if (inputFile.exists()){ // Load GMM from a file
                     logger.info("Loading model from:"+inputModel);
-                    GATKReport reportIn = new GATKReport(inputFile);
+                    final GATKReport reportIn = new GATKReport(inputFile);
 
                     // Read all the tables
-                    GATKReportTable amTable = reportIn.getTable("AnnotationMeans");
-                    GATKReportTable astdTable = reportIn.getTable("AnnotationStdevs");
+                    final GATKReportTable nmcTable = reportIn.getTable("NegativeModelCovariances");
+                    final GATKReportTable nmmTable = reportIn.getTable("NegativeModelMeans");
+                    final GATKReportTable nPMixTable = reportIn.getTable("BadGaussianPMix");
+                    final GATKReportTable pmcTable = reportIn.getTable("PositiveModelCovariances");
+                    final GATKReportTable pmmTable = reportIn.getTable("PositiveModelMeans");
+                    final GATKReportTable pPMixTable = reportIn.getTable("GoodGaussianPMix");
+                    final int numAnnotations = dataManager.getMeanVector().length;
 
-                    // Should have same number of means and standard deviations.
-                    assert(amTable.getNumRows() == astdTable.getNumRows() );
-
-                    GATKReportTable nmcTable = reportIn.getTable("NegativeModelCovariances");
-                    GATKReportTable nmmTable = reportIn.getTable("NegativeModelMeans");
-                    GATKReportTable nPMixTable = reportIn.getTable("BadGaussianPMix");
-                    GATKReportTable pmcTable = reportIn.getTable("PositiveModelCovariances");
-                    GATKReportTable pmmTable = reportIn.getTable("PositiveModelMeans");
-                    GATKReportTable pPMixTable = reportIn.getTable("GoodGaussianPMix");
-
-                    int numAnnotations = amTable.getNumRows();
                     goodModel = GMMFromTables(pmmTable, pmcTable, pPMixTable, numAnnotations);
                     engine.evaluateData(dataManager.getData(), goodModel, false);
                     negativeTrainingData = dataManager.selectWorstVariants();
                     badModel = GMMFromTables(nmmTable, nmcTable, nPMixTable, numAnnotations);
-                    logger.info("Loaded GMM from file:" + inputModel);
-                    dataManager.dropAggregateData(); // Don't need the aggregate data anymore so let's free up the memory
-                    engine.evaluateData(dataManager.getData(), badModel, true);
 
                 } else { // Generate the GMMs from scratch
+                    // Generate the positive model using the training data and evaluate each variant
                     goodModel = engine.generateModel(positiveTrainingData, VRAC.MAX_GAUSSIANS);
-                    //Utils.getRandomGenerator().setSeed(12878);
                     engine.evaluateData(dataManager.getData(), goodModel, false);
                     // Generate the negative model using the worst performing data and evaluate each variant contrastively
                     negativeTrainingData = dataManager.selectWorstVariants();
 
                     badModel = engine.generateModel(negativeTrainingData, Math.min(VRAC.MAX_GAUSSIANS_FOR_NEGATIVE_MODEL, VRAC.MAX_GAUSSIANS));
-                    dataManager.dropAggregateData(); // Don't need the aggregate data anymore so let's free up the memory
-                    //Utils.getRandomGenerator().setSeed(12878);
-                    engine.evaluateData(dataManager.getData(), badModel, true);
-
 
                     if (badModel.failedToConverge || goodModel.failedToConverge) {
                         throw new UserException("NaN LOD value assigned. Clustering with this few variants and these annotations is unsafe. Please consider " + (badModel.failedToConverge ? "raising the number of variants used to train the negative model (via --minNumBadVariants 5000, for example)." : "lowering the maximum number of Gaussians allowed for use in the model (via --maxGaussians 4, for example)."));
                     }
                 }
 
+                dataManager.dropAggregateData(); // Don't need the aggregate data anymore so let's free up the memory
+                engine.evaluateData(dataManager.getData(), badModel, true);
+
                 if (outputModel) {
                     GATKReport report = writeModelReport(goodModel, badModel, USE_ANNOTATIONS);
                     report.print(modelReport);
@@ -592,7 +581,7 @@ public class VariantRecalibrator extends RodWalker<ExpandingArrayList<VariantDat
      * @param numAnnotations    Number of annotations, i.e. Dimension of the annotation space in which the Gaussians live
      * @return  a GaussianMixtureModel whose state reflects the state recorded in the tables.
      */
-    private GaussianMixtureModel GMMFromTables(GATKReportTable muTable, GATKReportTable sigmaTable, GATKReportTable pmixTable, int numAnnotations){
+    private GaussianMixtureModel GMMFromTables(final GATKReportTable muTable, final GATKReportTable sigmaTable, final GATKReportTable pmixTable, final int numAnnotations){
         List<MultivariateGaussian> gaussianList = new ArrayList<>();
 
         int curAnnotation = 0;
@@ -642,7 +631,6 @@ public class VariantRecalibrator extends RodWalker<ExpandingArrayList<VariantDat
         double[] stdVector = {};
 
         for (GATKReportColumn reportColumn : astdTable.getColumnInfo() ) {
-            logger.info("Report column name is:" + reportColumn.getColumnName());
             if (reportColumn.getColumnName().equals("Standarddeviation")) {
                 stdVector = new double[astdTable.getNumRows()];
                 for (int row = 0; row < astdTable.getNumRows(); row++) {
@@ -663,7 +651,6 @@ public class VariantRecalibrator extends RodWalker<ExpandingArrayList<VariantDat
                 for (int row = 0; row < amTable.getNumRows(); row++) {
                     meanVector[row] =  Double.parseDouble((String) amTable.get(row, reportColumn.getColumnName()));
                 }
-                logger.info("Got mean Vector:" + Arrays.toString(meanVector));
             }
         }
 
@@ -690,7 +677,6 @@ public class VariantRecalibrator extends RodWalker<ExpandingArrayList<VariantDat
 
         for( final MultivariateGaussian gaussian : goodModel.getModelGaussians() ) {
             pMixtureLog10s[idx] = gaussian.pMixtureLog10;
-            logger.info("Good normalize PMix log 10 is:" + Double.toString(gaussian.pMixtureLog10) );
             gaussianStrings.add(Integer.toString(idx++) );
         }
 
@@ -703,7 +689,6 @@ public class VariantRecalibrator extends RodWalker<ExpandingArrayList<VariantDat
 
         for( final MultivariateGaussian gaussian : badModel.getModelGaussians() ) {
             pMixtureLog10sBad[idx] = gaussian.pMixtureLog10;
-            logger.info("Bad normalize PMix log 10 is:" + Double.toString(gaussian.pMixtureLog10));
             gaussianStrings.add(Integer.toString(idx++));
         }
         GATKReportTable badPMix = makeVectorTable("BadGaussianPMix", "Pmixture log 10 used to evaluate model", gaussianStrings, pMixtureLog10sBad, "pMixLog10", formatString, "Gaussian");