Snapshots
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Current analysis-backed training base
Repositories
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Unique repos visible to the trainer
Labeled rows
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Coverage Pending
Inactive 12m rate
Pending
Dataset label balance
Training Base
How the current OSS base looks before it reaches the model
Latest Artifact Coverage
Training-data results surfaced directly from the cached run
Rows in artifact
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Evaluation sample
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Latest hash
Pending
Artifact status
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This page is intentionally about the training base itself: which OSS projects are represented, how much data exists, how much is labeled, how imbalanced the held-out slice is, and which features flow into the model.
Effect Sizes
Which training signals separate active from inactive projects
r compares inactive vs active labels; negative means the metric is more common in active/healthy projects.
Loading effect sizes from the staged training base.
Training OSS Projects
Ranked repositories in the current base
Repository submissions use the same scoring surface as these base projects, so a newly searched repo can be compared against the captured OSS population.
Run repository analyses first to populate the visible OSS training base.
Feature Inventory
Latest training feature set
Run training once to surface the feature inventory from the latest cached artifact.