feat(recommendation): For You exploration attribution — taste vs fresh picks

Milestone #127 step 2 (#1249). For You deliberately blends two
populations — a head of top-scored taste picks and a tail sampled from
deeper ranking (the freshness injection) — but the metrics judged it as
one blob, so its skip rate couldn't distinguish "the taste engine is
missing" from "the freshness tax is too high". That number decides the
exploration share before we tune it.

- Migration 0038: nullable pick_kind ('taste'|'fresh') on both
  playlist_tracks (stamped at snapshot build) and play_events (frozen at
  play-ingestion — the snapshot rebuilds daily, so attribution cannot be
  reconstructed at read time).
- Builder: pickHeadAndTail marks head=taste / tail=fresh; the small-pool
  fallback is all taste (top-N-by-score IS the taste mechanism). Other
  variants persist NULL.
- Ingestion: for_you plays (live + offline replay) look the track up in
  the user's current snapshot; not found → unattributed, never guessed.
- Metrics: For You's row gains a breakdown (taste / fresh / earlier
  unattributed plays), parent row stays the sum; web card renders the
  sub-rows indented with the same baseline deltas + low-data dimming.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TsF3cNoKrqCYsU78cXC8U6

internal/db/dbq/recommendation_metrics.sql.go

@@ -15,6 +15,7 @@ const recommendationSourceMetricsForUser = `-- name: RecommendationSourceMetrics
 
 SELECT
     pe.source,
+    pe.pick_kind,
     count(*)::bigint AS plays,
     count(*) FILTER (WHERE pe.was_skipped)::bigint AS skips,
     count(pe.completion_ratio)::bigint AS completion_n,
@@ -22,7 +23,7 @@ SELECT
 FROM play_events pe
 WHERE pe.user_id = $1
   AND pe.started_at > now() - ($2::float8 * INTERVAL '1 day')
-GROUP BY pe.source
+GROUP BY pe.source, pe.pick_kind
 ORDER BY plays DESC
 `
 
@@ -33,6 +34,7 @@ type RecommendationSourceMetricsForUserParams struct {
 
 type RecommendationSourceMetricsForUserRow struct {
 	Source        *string
+	PickKind      *string
 	Plays         int64
 	Skips         int64
 	CompletionN   int64
@@ -50,6 +52,8 @@ type RecommendationSourceMetricsForUserRow struct {
 // avg_completion correctly.
 // $1 user_id, $2 window_days. plays/skips are counts; avg_completion is the
 // mean completion ratio over the completion_n plays that recorded one.
+// pick_kind splits For You plays into taste/fresh/unattributed (#1249);
+// it is NULL for every other source, so those still group to one row.
 func (q *Queries) RecommendationSourceMetricsForUser(ctx context.Context, arg RecommendationSourceMetricsForUserParams) ([]RecommendationSourceMetricsForUserRow, error) {
 	rows, err := q.db.Query(ctx, recommendationSourceMetricsForUser, arg.UserID, arg.Column2)
 	if err != nil {
@@ -61,6 +65,7 @@ func (q *Queries) RecommendationSourceMetricsForUser(ctx context.Context, arg Re
 		var i RecommendationSourceMetricsForUserRow
 		if err := rows.Scan(
 			&i.Source,
+			&i.PickKind,
 			&i.Plays,
 			&i.Skips,
 			&i.CompletionN,