27 lines
1.3 KiB
Markdown
27 lines
1.3 KiB
Markdown
# M1 Validation Gate
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Before widening past the hard sample, trip-cluster must clear a quantified bar on a
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**deliberately hard slice** — a GPS-poor, multi-year, low-density window (a well-remembered
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old trip plus its surrounding everyday photos), **not** the easy phone-era last trip.
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## Protocol
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1. `categorize ingest --from <hard-window-start> --to <hard-window-end>` (or `--tag <old-trip>`).
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2. `categorize cluster`, then `categorize serve`.
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3. Hand-label the slice: the true trip boundaries + which surrounding photos are non-trip.
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4. Compare candidate clusters against the labels.
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## Acceptance bar (record actual numbers per run)
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| Metric | Definition | Target |
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|--------|------------|--------|
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| Trip-boundary precision | proposed boundaries that are real | ≥ 0.8 |
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| Trip-boundary recall | real boundaries proposed | ≥ 0.8 |
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| Coverage-flag recall | in-window missing-tag assets surfaced | ≥ 0.9 |
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| Over-split rate | extra clusters per real trip | ≤ 0.5 |
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| False-cluster rate | clusters that are pure noise | ≤ 0.1 |
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If unmet, tune `--gap-factor` / seed-span / confidence thresholds — do **not** widen the
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backlog. See the F4 open question (everyday cluster blow-up): observe the real cluster
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count on a representative subset first, then choose the surfacing/collapsing strategy.
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