diff --git a/CLAUDE.md b/CLAUDE.md
index 77d12f55..de16ff36 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -221,6 +221,10 @@ User submits QSO → enqueue_for_qso() → weather/hrrr/terrain/iemre workers
- **Always run `cargo clippy --all-targets -- -D warnings` before committing Rust changes.** The Forgejo CI step runs clippy with warnings-as-errors and a passing local `cargo build` does NOT catch lints (e.g. `manual_range_contains`, `iter_kv_map`, `useless_format`). A clippy failure blocks the image build and the deploy. The Rust precommit recipe is in the `Commands` section.
- The CI build pipeline runs `cargo clippy --all-targets -- -D warnings && cargo test --release && cargo build --release`. Match this locally — `cargo build` alone is not enough.
+### `/about` page maintenance
+- Refresh `lib/microwaveprop_web/live/about_live.ex` whenever **algorithm structure**, **factor count or weights**, **major data sources**, **schema scale claims (e.g. "58k+ contacts")**, or **roadmap items** change. The dashboard counts auto-refresh from the DB; the prose blocks (overview, "How it works", "How it's built", "What's next") do not.
+- The page renders a per-table count via `count_exact/1` and `count_estimate/1`. Each query is wrapped individually so a missing dev table doesn't blank the whole dashboard — keep that pattern when adding new stats.
+
### Deployment
- Production runs on Kubernetes (`home-cluster` context, `prop` namespace) — use `kubectl -n prop ...`
- Deployment name `prop` (3 replicas). Inspect with `kubectl -n prop get pods`, `kubectl -n prop logs deploy/prop`, `kubectl -n prop exec deploy/prop -- ...`
diff --git a/lib/microwaveprop_web/live/about_live.ex b/lib/microwaveprop_web/live/about_live.ex
index 20c6f45f..56d460ee 100644
--- a/lib/microwaveprop_web/live/about_live.ex
+++ b/lib/microwaveprop_web/live/about_live.ex
@@ -76,27 +76,29 @@ defmodule MicrowavepropWeb.AboutLive do
profiles. We derive refractivity profiles, minimum refractivity
gradient, ducting detection, boundary-layer depth, and precipitable
water for every grid cell. ASOS surface obs, 12-hourly upper-air
- soundings, and gridded IEMRE reanalysis fill in the gaps. A 9-factor
- composite score is written out for every 0.125° cell across CONUS,
- for each hour of an 18-hour forecast.
+ soundings, and gridded IEMRE reanalysis fill in the gaps. A
+ 10-factor composite score is written out for every 0.125° cell
+ across CONUS, for each hour of an 18-hour forecast.
The contacts.
- 58k+ amateur microwave QSOs, each tagged with the atmosphere at
- both ends of the path (and along it) at the time the contact
- happened. That's a ground-truth dataset we can actually fit against
- — "when you had this score, how far did the contact go, and did it
- happen at all?"
+ {format_count(@stats.contacts)}+ amateur microwave QSOs, each
+ tagged with the atmosphere at both ends of the path (and along it)
+ at the time the contact happened. That's a ground-truth dataset we
+ can actually fit against — "when you had this score, how far did
+ the contact go, and did it happen at all?"
The scoring is a weighted sum of ten factors: rain, humidity,
precipitable water (PWAT), season, refractivity gradient, pressure,
- T–Td depression, sky cover, wind, and time of day. Weights were
- calibrated via gradient descent against 5,000 QSOs. The physics
- changes by frequency: humidity helps at 10 GHz (more refractivity)
- and hurts at 24+ GHz (absorption). Refractivity now uses native
- HRRR hybrid-sigma levels (10–50 m resolution) for duct detection.
+ T–Td depression, sky cover, wind, and time of day — weights
+ recalibrated via gradient descent against the contact dataset. The
+ physics changes by frequency: humidity helps at 10 GHz (more
+ refractivity) and hurts at 24+ GHz (absorption). Refractivity uses
+ native HRRR hybrid-sigma levels (10–50 m resolution) for duct
+ detection, with a pressure-level fallback (~250 m) when the native
+ data isn't available.
@@ -233,12 +235,6 @@ defmodule MicrowavepropWeb.AboutLive do
# percent, which is plenty for "big number on a marketing page."
@estimate_tables ~w(hrrr_profiles propagation_scores surface_observations)
- @stat_keys ~w(
- contacts weather_stations surface_observations soundings hrrr_profiles
- iemre_observations terrain_profiles propagation_scores beacons
- commercial_samples
- )a
-
defp fetch_stats do
%{
contacts: count_exact("contacts"),
@@ -252,22 +248,25 @@ defmodule MicrowavepropWeb.AboutLive do
beacons: count_exact("beacons"),
commercial_samples: count_exact("commercial_samples")
}
- rescue
- _ -> Map.new(@stat_keys, fn k -> {k, 0} end)
end
+ # Each count is wrapped individually so a missing table in dev (or a
+ # transient query failure) never blanks the entire dashboard. The
+ # previous outer try/rescue zeroed all 10 numbers if any single one
+ # raised — every stat read 0 on environments lacking hrrr_profiles
+ # or propagation_scores.
defp count_exact(table) do
- %Postgrex.Result{rows: [[count]]} =
- Repo.query!("SELECT count(*) FROM #{table}")
-
- count
+ case Repo.query("SELECT count(*) FROM #{table}") do
+ {:ok, %Postgrex.Result{rows: [[count]]}} -> count
+ _ -> 0
+ end
end
defp count_estimate(table) when table in @estimate_tables do
- %Postgrex.Result{rows: [[estimate]]} =
- Repo.query!("SELECT reltuples::bigint FROM pg_class WHERE relname = $1", [table])
-
- max(estimate || 0, 0)
+ case Repo.query("SELECT reltuples::bigint FROM pg_class WHERE relname = $1", [table]) do
+ {:ok, %Postgrex.Result{rows: [[estimate]]}} -> max(estimate || 0, 0)
+ _ -> 0
+ end
end
defp format_count(n) when is_integer(n) and n >= 1_000_000 do