🔬 Learning Integrity — Is the model actually learning?
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Learning Quality Score
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How to read this:
Each source gets a statistical grade based on sample size, significance, and calibration.
Sources with grade F or D have NO weight changes applied — we refuse to "learn" from
noise. Sources need at least 20 settled picks with p<0.10 before any
weight movement, and 50+ picks with CLV>=+2pp and Brier<0.24 for an A grade
(trusted edge proven). This is the difference between real ML and curve-fitting.
Per-Source Statistical Grade
Source
Grade
Status
Wilson 95% CI
p-value
Brier
ECE
CLV
N Settled
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Cross-Source Overlap (independence check)
If two sources have >70% pick overlap, they're not independent — we're double-counting the same signal.
Source A
Source B
Jaccard
Overlap
Status
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Concept Drift Watch
Has last-30d hit rate diverged from prior 30d? If yes, model regime may have changed.
Source
Recent N
Recent Hit%
Prior Hit%
Δ pp
Drifting?
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