# Utah Vehicle Health dashboard demo script

Target length: **2 minutes 45 seconds**, embedded in the
[10-minute presentation](presentation_outline.md).

## Before the audience arrives

1. Put the presenting computer and demo computer on the same trusted network.
2. Stop any older dashboard process, then from the repository root run
   `node dashboard/server.mjs --lan`.
3. On the demo computer, open the printed **Other devices** URL for the shared
   Wi-Fi or Ethernet interface with `/#overview` appended. On the presenting
   computer, use the printed **This computer** URL.
4. Confirm the header says **Validated sample aggregates**. If an older copy of
   the JavaScript was previously loaded, hard-refresh the page once.
5. Confirm the private-sample development-preview banner is visible.
6. Visit all four routes: Overview, Sample cohorts, Model & benchmark, and Data
   & methods.
7. Reset Sample cohorts and leave its sort on largest support.

Do not open developer tools, private files, model artifacts, database clients,
or environment variables during the presentation. Stop the server with
`Ctrl-C` when the demo is over.

## Live talk track

### 0:00-0:25 — Establish the boundary

**Action:** Start on **Overview**. Point to the development-preview banner before
pointing to a chart.

**Say:**

> The most important element is this banner. Every value in the dashboard comes
> from the private 10,000-vehicle development sample. These are suppression-
> reviewed sample aggregates, not population estimates, and they cannot support
> individual decisions or county rankings.

### 0:25-1:00 — Explain the overview

**Action:** Point to published support and the observed first-attempt non-pass
trend, then the coverage map and vehicle-age chart. Do not rank counties by
outcome.

**Say:**

> The overview reports rounded support and sample first-attempt outcomes for
> returning vehicles. Non-pass combines fail, reject, and abort. The trend is
> descriptive of sampled records only. This county graphic communicates feed
> availability: gray means unavailable, not zero and not better. The age pattern
> is also an observed sample association, not a causal claim or diagnosis.

### 1:00-1:35 — Use Sample cohorts safely

**Action:** Open **Sample cohorts**. Search for `Toyota` or another currently
supported make. Leave sorting on largest support. Point to rounded support and
the “observed sample non-pass” label.

**Say:**

> This view contains only prior make-and-model cohorts that cleared the
> publication thresholds. Selected source abbreviations and trim labels are
> combined for new model families, while earlier published cohorts keep stable
> definitions. I can search supported cohorts, but these are observed
> development-sample associations—not reliability grades, rankings, or
> recommendations. Unsupported slices are not inferred in the browser, and
> suppressed rows are absent rather than hidden.

**Action:** Reset the search before leaving the view.

### 1:35-2:15 — Separate the final model from its benchmark

**Action:** Open **Model & benchmark**. Point first to the role badges, then the
2024 calibration-fit scope strip, then the “No vehicle-level prediction service”
boundary.

**Say:**

> The governance decision is explicit: calibrated logistic regression is the
> final prototype model, while the boosted tree is benchmark-only. The numbers
> shown here are pre-2025 checks on the same 2024 sample partition used to fit
> calibration. They are not independent final-performance or population
> estimates, so we do not select between the cards from these values. The
> one-time 2025 comparison shown earlier favored logistic and stays in the report
> and presentation, not the browser bundle. This finished dashboard contains no
> vehicle lookup, personal inputs, or row-level prediction output.

### 2:15-2:40 — Close on method and privacy

**Action:** Open **Data & methods**. Briefly point to Prediction unit, the
chronological timeline, evidence status, and the privacy flow.

**Say:**

> The method page makes the contract visible: the target is the first attempt of
> a returning vehicle's next episode, and every feature ends before that episode.
> Training, tuning, and calibration are chronological. It also records that 2025
> was opened once after choices were frozen. Finally, the public path ends in
> suppressed summaries. No private rows or database connection reach this site.

### 2:40-2:45 — Transition

**Say:**

> That is the prototype: useful sample evidence, with model and privacy
> boundaries kept visible.

Return to the slide deck's privacy architecture.

## If something goes wrong

If the dashboard shows **data unavailable**, do not bypass validation or edit
JSON. Say:

> The site rejected an aggregate-contract mismatch and is failing closed, so it
> shows no estimates. That behavior is part of the privacy and integrity design.

Then continue with a backup screenshot or slides. If a cohort search returns no
rows, explain that unsupported or suppressed cohorts are intentionally absent
and reset the filter. Never substitute remembered values or improvise a vehicle-
level example.