Verifying ClearCase files have been labeled properly

wish of those help Names starting with numbers such as 3ZSJ_ALA_A_142 are not syntactically valid.
When you call read.csv it has an argument check.names which will check the names for syntactic validity, and adjust if necessary (the default is check.names = TRUE)

foo <- subset(T1, X3ZSJ_ALA_A_142 == '10')

 T2 = read.csv('foo.csv', header=TRUE, check.names = FALSE )
    foo <- subset(T1, `3ZSJ_ALA_A_142` == '10')

I hope this helps . Because of the way prototypal inheritance works in JavaScript, you can't use primatives on the scope for 2-way databinding. Therefore the way to fix this is to change selectedValue to an object...

angular.module('app', [])
.controller('controller', function($scope) {
    $scope.selectedValue = { value: 'FOO' };
    $scope.radios = [
        { label: 'foo', value: 'FOO' },
        { label: 'bar', value: 'BAR' }
    ];
})

<div data-ng-controller="controller">
    <div
      data-ng-repeat="radio in radios"
      data-ng-model="selectedValue.value"
      data-name="radio1"
      data-label="{{radio.label}}"
      data-value="{{radio.value}}"
      data-labeled-radio></div>
    <br>
    selected value: {{selectedValue.value}}
</div>

hope this fix your issue
Unfortunately its only me who can see the contents of vob with this my label

element /vob_name/... label_name -mkbranch BRANCH_NAME 
element /vob_name/... /main/LATEST -mkbranch BRANCH_NAME 

will be helpful for those in need No, its impossible. The LABEL_B of Next day(including LABEL_A) are different from LABEL_B of your created. You can't simply split it.
Why you insist on replacing LABEL_B to use it? The simplest way is to create a new LABEL such as LABEL_D for $/Project/Folder. And then just merge it in LABEL_D and you will get LABEL_C alone.

I hope this helps you . It is not going to give you an improved accuracy when using less labels. If you split you data in order to delete the labels from one group and use that with a semi-supervised learning, it will just reduce your accuracy. The purpose of semi-supervised is that the process of labeling massive amounts of data for supervised learning is extremely time-consuming and expensive, so if you need more data (that you already have), then you can use techniques to use unlabeled data. Before even thinking about the coding in Java, can you develop a bit more your idea of why you were thinking of this?