StatsMapped Public Data

1. `stat_key` alone (no `pair_key`, no `stat_key_a`/`stat_key_b`): ranks
   every area at one geography level by its latest figure for that stat,
   for one country -- e.g. "which counties have the highest median sale
   price" (country="ireland"). `stat_key` comes from `query_data`'s
   dataset-listing mode, for the SAME country. `level` omitted uses this
   ranking's own default level; pass one of that dataset's own
   `compatible_levels` for a different one -- a level this ranking
   doesn't have registered returns an empty list rather than an error.
   Where the underlying stat has no honest per-area denominator (crime,
   homelessness, live_register and similar -- StatsMapped's own
   RANKING_NO_DENOMINATOR_STATS), each row's `rate_per_1000` is the real
   figure to rank/compare by, not `latest_value`, which is a raw count
   dominated by area population size. Always carry forward every entry
   in `caveats` when using a row in an answer.
2. `pair_key` alone: full detail for one registered comparison pair --
   each axis's label, unit and publisher, the correlation stats (r, rho,
   and a leave-one-out sensitivity range naming the single most
   influential area), and caveats. `pair_key` comes from mode 4's own
   response, for the SAME country.
3. Both `stat_key_a` and `stat_key_b` given: does StatsMapped have a
   registered, hand-vetted comparison between these two stats? Registry-
   backed only -- never computes a fresh correlation for an arbitrary
   pair. Both stat_keys come from `query_data`'s dataset-listing mode,
   for the SAME country. `verdict` is one of `"SUPPORT"` (a real,
   hand-vetted registered pair with no open caveats -- may be treated as
   a confirmed relationship), `"QUALIFY"` (hand-vetted, but the evidence
   carries real caveats -- e.g. no robustness check for outliers, or an
   unverified geography-level join; read `uncertainty` and `reasons`
   before presenting it as confirmed), `"REJECT"` (a real structural
   impossibility or a human-vetted "no" -- the two stats share no
   geography level at all, or a reviewer rejected this exact pairing),
   or `"INSUFFICIENT"` (not registered, not ruled out either --
   StatsMapped genuinely hasn't vetted this pair; never treat this as
   "probably comparable"). `uncertainty` names 4 separate dimensions
   (data_quality, comparability, statistical_strength,
   causal_strength) -- `causal_strength` is always `"not_established"`,
   since no comparison here implies causation regardless of verdict.
   `comparable` (DEPRECATED, kept only for callers that haven't
   migrated) collapses `verdict` to the old 3-way yes/no/unknown --
   `"yes"` for both `SUPPORT` and `QUALIFY` (both mean "hand-vetted",
   the old `comparable` meaning this field has always carried; the
   caveats a `QUALIFY` pair carries live in `uncertainty`/`reasons`,
   not in demoting `comparable`), `"no"` for `REJECT`, `"unknown"` for
   `INSUFFICIENT`. Prefer `verdict` directly when you need to
   distinguish a fully-confirmed `SUPPORT` from a caveated `QUALIFY`.
   Read `reasons` before presenting any answer other than `"SUPPORT"`
   as unqualified.
4. None of the above given: lists every registered cross-dataset
   comparison pair for one country -- e.g. "median sale price vs new
   dwelling completions per 1,000 residents". A small, hand-curated set,
   not an arbitrary-pair engine: pass one of the returned `pair_key`
   values to mode 2 for the real correlation and axis detail.

   `level` is only meaningful together with `stat_key` (mode 1); giving
   it without `stat_key` raises rather than silently dropping it and
   falling through to mode 4's unrelated pair listing.

1. Neither `area_id` nor `dataset`: lists every dataset (stat) StatsMapped
   tracks for one country ('ireland' or 'united-kingdom'), with its key,
   human label, and which geography levels it can be shown at. Ireland and
   the UK track genuinely different datasets -- call this first for the
   right country before assuming a stat_key exists there, to find the
   right `stat_key` for `compare`'s ranking mode.
2. `area_id` given, `dataset` omitted: lists every dataset available for
   that one area (e.g. "county:kerry" for Ireland, "uk:lad:e09000033" for
   the UK), with its latest figure, year-on-year change, and caveat labels
   only (not full caveat text -- use mode 3 for the full detail on any one
   dataset that matters). `area_id` comes from `list_areas`; `country`
   must match whichever country that call used, or this simply 404s
   ("unknown geography").
3. Both `area_id` and `dataset` given: full detail for one dataset in one
   area -- the latest figure, a written summary, full caveat text, and (if
   `history_months` is set) recent history. `dataset` is a `series_key`
   from mode 2's own response. `history_months` means actual months of
   history (0 = everything) -- e.g. 24 returns 2 years of an annual
   series, not 24 years. `country` must match `area_id`'s own country.

   `dataset` and `history_months` are only meaningful together with
   `area_id` (and, for `history_months`, `dataset` too, since it only
   applies to mode 3); giving either without its real precondition
   raises rather than silently dropping the argument and dispatching to
   the wrong mode.