Keep each face's quality, and never compare against a poor one

The embedder's raw output has a length, and the length is a reading of
how recognisable the crop was: a blur, an occlusion or a hard profile
comes out short. Normalising threw it away. A short vector sits near
the middle of the sphere and matches a little of everyone, which is how
one bad crop bridges two people in a grouping pass.

So the length is kept — the store now holds the raw vector, re-normalised
on load, with the length beside it as `faces.quality` — and a face under
MIN_GALLERY_QUALITY (14) is a probe: measured against the gallery and
placed where it fits, but never what another face is measured against.
Two probes are never paired, and a probe is nobody's evidence for a
confidence. The People screen shows the number as "Quality 17.3", dimmed
below the floor.

Faces indexed before this stored unit vectors and have no reading; they
are admitted to the gallery, and schema V14 forgets the run marker of
every image holding one so the next indexing pass measures them. A
peer's unmeasured shard faces are not adopted, or a sync would write
that marker back.

docs/catalog.md

@@ -742,7 +742,12 @@ CREATE TABLE faces (
     x REAL NOT NULL, y REAL NOT NULL, w REAL NOT NULL, h REAL NOT NULL,
     landmarks  BLOB,                 -- 5 × (x, y) f32, the alignment input
     detector_confidence REAL NOT NULL,
-    embedding  BLOB NOT NULL,        -- 512 × f16, L2-normalised
+    embedding  BLOB NOT NULL,        -- 512 × f16, the raw model output; re-normalised on load
+    -- Length of that vector: the model's own reading of how recognisable the
+    -- crop was, and the gate on whether this face may be compared *against*
+    -- (faces.md §6, §9). NULL for a face stored as a unit vector before it
+    -- was kept.
+    quality    REAL,
     -- Which model produced this. An embedding is only comparable to others
     -- from the same model; mixing them silently yields nonsense similarities.
     model_id   TEXT NOT NULL,