98 lines
4.3 KiB
Markdown
98 lines
4.3 KiB
Markdown
# Bolt dashboard specification
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## Product language
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Use **Utah Vehicle Health** as the brand, but call the modeled quantity
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**next-episode non-pass risk**. The estimator is a cohort estimate, not a
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diagnosis, certification, or guarantee.
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## Four-page MVP
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The sections below describe the full product target. The checked-in
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development preview intentionally implements a narrower safe subset: binary
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pass/non-pass aggregates, supported make/model scorecards, age bands, coverage
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quality, and pre-2025 model diagnostics. Four-class charts, uncertainty
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intervals, adjusted scorecards, and the prediction lookup remain disabled until
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their own reviewed aggregate assets exist.
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### Overview
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- Eligible inspections, pass rate, non-pass rate, and covered-period KPIs
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- Quarterly pass/fail/reject/abort trend with blanks shown separately
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- Covered-county map; unavailable counties remain gray
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- Non-pass risk versus vehicle age with intervals
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- Clear notices for partial periods and limited feed coverage
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### Reliability explorer
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- Search supported canonical make/model cohorts
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- Compare up to three cohorts across vehicle-age bands
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- Observed versus model-adjusted risk toggle
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- Outcome-mix bar and uncertainty-aware ranked dot plot
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- County, make/model, age band, fuel, program, and period filters
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- Support size and interval displayed for every estimate
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### Next-test risk estimator
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Inputs are coarsened, non-identifying attributes: county, supported make/model,
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vehicle-age band, fuel, prior episode outcome, time-since-prior band, season,
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and approved program category.
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Output a calibrated non-pass probability, uncertainty interval, relevant
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baseline, and aggregate factor contributions. Never request VIN, plate, exact
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address, station, free text, or current-test diagnostics.
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### Data and methods
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- Coverage timeline and source/year missingness heatmap
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- Episode and target definitions
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- `slc`/`slco` source-era explanation
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- Temporal split, PR-AUC, Brier score, and calibration plot
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- Subgroup/source-era performance
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- Leakage controls, DMV gaps, partial periods, and limitations
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## Public data contract
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| Dataset | Safe grain |
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| --- | --- |
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| `data_manifest` | Data cutoff, deterministic release ID, model versions, definitions and exclusions |
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| `overview_period_county` | Quarter/year × public county with rounded support and outcome rates |
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| `cohort_scorecard` | Approved make/model × age band, optionally coarsened county/fuel |
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| `age_risk_curve` | Approved cohort × age point/band with risk, interval and support |
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| `prediction_lookup` | Only supported coarsened input combinations and calibrated outputs |
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| `filter_catalog` | Publishable categories and valid combinations |
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| `model_diagnostics` | Approved partition-level metrics; locked metrics require a separate release gate |
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| `coverage_quality` | Source era × year volume, blank rate, linkage and availability |
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Use purpose-built outputs rather than a single high-dimensional browser cube.
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Bolt receives only these sanitized, versioned assets—never countydata
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credentials or private analytical rows.
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## Publication controls
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- Suppress cells below 100 eligible inspections.
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- Also require at least 100 distinct private vehicle tokens in every published
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cell; tokens and distinct counts never enter the public asset.
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- Suppress when an outcome or its complement has fewer than 10 records.
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- Require at least 10 distinct vehicles contributing each binary class.
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- Apply complementary suppression so totals cannot reconstruct hidden cells.
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- Combine rare categories, coarsen model years, and round displayed counts.
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- Recheck thresholds after every filter combination.
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- Do not include suppressed rows in browser bundles, API responses, downloads,
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analytics logs, or hidden chart layers.
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- Downloads contain only the sanitized summary currently displayed.
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## Visual direction
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Use a restrained Utah/desert palette: teal pass, red fail, amber reject, purple
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abort, and gray missing. Use probability bars, calibrated dot plots, confidence
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bands, and cohort comparisons instead of gauges. Do not rely on color alone.
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## Stretch pages
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- Failure-to-pass journeys with funnels, attempts-to-pass and survival curves
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- Four-class outcome probabilities
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- Source-scoped OBD early-warning analysis
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- Automated aggregate refresh with a dedicated read-only role
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- Model-drift monitoring
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