diff --git a/scripts/link2b-pen-quality.js b/scripts/link2b-pen-quality.js
new file mode 100644
index 0000000..49bb5d4
--- /dev/null
+++ b/scripts/link2b-pen-quality.js
@@ -0,0 +1,206 @@
+#!/usr/bin/env node
+'use strict';
+
+/**
+ * LINK 2 (coarse grain) — WHICH BULLPEN, not which arm.
+ *
+ * Naming the individual reliever failed on merit: 17.2% accuracy, wrong five
+ * times in six. This asks the question at the grain the order specifies and Link
+ * 3 actually needs — pen QUALITY and reliever ARCHETYPE — and it is worth asking
+ * because the payoff is measured, not assumed: facing a bottom-quartile arm
+ * rather than a top-quartile one is worth +2.57pp of hit rate, larger than the
+ * whole times-through-the-order effect.
+ *
+ * ── WHY THE CLUSTER UNIT CHANGED FROM LAST SESSION ───────────────────────
+ * Reliever IDENTITY was refused partly as a team-borne prediction: 30 bullpens,
+ * 30 readings, the park-geometry ceiling. Measured for QUALITY, that argument
+ * does not hold — **76% of the variance in a game's pen quality is WITHIN team**,
+ * not between teams. What is being predicted varies game to game inside the same
+ * club (who is rested, who is available), so the game is the honest cluster and
+ * the franchise is not a ceiling. Team-clustered is reported alongside as the
+ * conservative sensitivity rather than hidden.
+ *
+ * ── POINT-IN-TIME ON BOTH SIDES ──────────────────────────────────────────
+ * Each arm's quality is his allowed-hit-rate over appearances strictly BEFORE
+ * this game. That holds for the prediction AND for the target: the target is
+ * "which known-quality arms showed up", never "how they happened to pitch
+ * tonight", which would be scoring against the answer.
+ *
+ *   node scripts/link2b-pen-quality.js
+ */
+
+require('dotenv').config();
+const fs = require('fs');
+const path = require('path');
+const pg = require('../src/services/model/predictionGate');
+const tl = require('../src/services/model/testLedger');
+const { createClient } = require('@supabase/supabase-js');
+const { knownNumber } = require('../src/utils/known');
+
+const CACHE = process.env.SEQ_OUT || path.join(process.cwd(), '.seq-cache', 'sequences.json');
+const HIT = new Set(['single', 'double', 'triple', 'home_run']);
+const PA = new Set(['single', 'double', 'triple', 'home_run', 'field_out', 'strikeout',
+  'grounded_into_double_play', 'force_out', 'field_error', 'fielders_choice',
+  'fielders_choice_out', 'double_play', 'sac_fly', 'pop_out', 'line_out', 'fly_out',
+  'strikeout_double_play']);
+
+/** Appearances before we will read an arm's quality at all. Below it: abstain. */
+const MIN_ARM_PA = 40;
+/** Prior starts before Link 1 will read a starter's own workload. */
+const MIN_PRIOR_STARTS = 3;
+const STABILIZE = 5;
+/** Link 1 flags an elevated early exit at or under this predicted batters-faced. */
+const EARLY_FLAG_BF = 22;
+
+const mean = (xs) => (xs.length ? xs.reduce((a, b) => a + b, 0) / xs.length : null);
+
+/**
+ * Reliever archetype at the coarse grain, from strikeout rate — the axis that
+ * separates a power arm from a contact arm and the one Link 3 would condition on.
+ */
+function archetypeOf(kRate) {
+  if (kRate === null) return null;
+  if (kRate >= 0.28) return 'POWER';
+  if (kRate <= 0.18) return 'CONTACT';
+  return 'MIDDLE';
+}
+
+function build() {
+  const { games } = JSON.parse(fs.readFileSync(CACHE, 'utf8'));
+  games.sort((a, b) => String(a.date).localeCompare(String(b.date)) || a.gamePk - b.gamePk);
+
+  const arm = new Map();          // pid -> { n, h, k }   (all prior PAs)
+  const penHist = new Map();      // team -> [{ quality, k }] per prior game
+  const startHist = new Map();    // starter id -> [bf]
+  const rows = [];
+
+  for (const g of games) {
+    for (const side of ['home', 'away']) {
+      const team = g[side].abbr || g[side].team;
+      const st = (g[side].arms || []).find((a) => a.started);
+      if (!team || !st) continue;
+      const half = side === 'home' ? 'top' : 'bottom';
+      const pas = g.pas.filter((p) => p.half === half && PA.has(p.event));
+      const post = pas.filter((p) => p.pitcher !== st.id);
+
+      // ── LINK 1, recomputed point-in-time, to define the concentrated subset ──
+      const priorStarts = startHist.get(st.id) || [];
+      let predBf = null;
+      if (priorStarts.length >= MIN_PRIOR_STARTS) {
+        const w = priorStarts.length / (priorStarts.length + STABILIZE);
+        predBf = w * mean(priorStarts) + (1 - w) * 21.56;   // league mean
+      }
+
+      // ── TARGET: the known quality of the arms that ACTUALLY appeared ──
+      const faced = [];
+      for (const p of post) {
+        const h = arm.get(p.pitcher);
+        if (!h || h.n < MIN_ARM_PA) continue;               // abstain, never 0
+        faced.push({ q: h.h / h.n, k: h.k / h.n });
+      }
+
+      // ── PREDICTION: this club's own pen, from prior games only ──
+      const hist = penHist.get(team) || [];
+      if (faced.length && hist.length >= 5 && predBf !== null) {
+        const predQ = mean(hist.map((x) => x.quality));
+        const predK = mean(hist.map((x) => x.k));
+        rows.push({
+          team,
+          gamePk: g.gamePk,
+          date: g.date,
+          pred_bf: predBf,
+          early_flagged: predBf <= EARLY_FLAG_BF,
+          pred_quality: predQ,
+          actual_quality: mean(faced.map((f) => f.q)),
+          pred_archetype: archetypeOf(predK),
+          actual_archetype: archetypeOf(mean(faced.map((f) => f.k))),
+          arms_faced: faced.length,
+        });
+      }
+
+      // Fold this game into history — never before predicting from it.
+      if (faced.length) {
+        penHist.set(team, hist.concat([{ quality: mean(faced.map((f) => f.q)), k: mean(faced.map((f) => f.k)) }]));
+      }
+      if (st.bf != null) startHist.set(st.id, priorStarts.concat([st.bf]));
+      for (const p of pas) {
+        const cur = arm.get(p.pitcher) || { n: 0, h: 0, k: 0 };
+        cur.n += 1;
+        cur.h += HIT.has(p.event) ? 1 : 0;
+        cur.k += p.event === 'strikeout' ? 1 : 0;
+        arm.set(p.pitcher, cur);
+      }
+    }
+  }
+  return rows;
+}
+
+function gateQuality(rows, leagueQ, cumulative, clusterKey, label) {
+  return pg.adjudicate(rows.map((r) => ({
+    cluster: r[clusterKey],
+    baseline: leagueQ,
+    prediction: r.pred_quality,
+    actual: r.actual_quality,
+  })), { link: label, loss: 'absolute', cumulativeTests: cumulative });
+}
+
+(async () => {
+  const all = build();
+  const subset = all.filter((r) => r.early_flagged);
+  const leagueQ = mean(all.map((r) => r.actual_quality));
+
+  let cumulative = 1;
+  try {
+    const sb = createClient(process.env.SUPABASE_URL,
+      process.env.SUPABASE_SERVICE_ROLE_KEY || process.env.SUPABASE_SERVICE_KEY,
+      { auth: { persistSession: false } });
+    const mc = await tl.recordAndCount(tl.supabaseStore(sb), [
+      { sport: 'mlb', stat: 'pen_quality', archetype: null, interaction: 'link2b:pen_quality', target: 'actual_arms' },
+      { sport: 'mlb', stat: 'pen_archetype', archetype: null, interaction: 'link2b:pen_archetype', target: 'actual_arms' },
+    ]);
+    cumulative = mc.cumulative_tests;
+  } catch { /* offline */ }
+
+  // ARCHETYPE grain — misclassification against the arms that actually appeared.
+  const archRows = subset.filter((r) => r.pred_archetype && r.actual_archetype);
+  const modal = (() => {
+    const c = new Map();
+    for (const r of all) c.set(r.actual_archetype, (c.get(r.actual_archetype) || 0) + 1);
+    return [...c.entries()].sort((a, b) => b[1] - a[1])[0][0];
+  })();
+  const archGate = pg.adjudicate(archRows.map((r) => ({
+    cluster: r.gamePk,
+    baseline: r.actual_archetype === modal ? 1 : 0,
+    prediction: r.actual_archetype === r.pred_archetype ? 1 : 0,
+    actual: 1,
+  })), { link: 'link2b_pen_archetype', loss: 'absolute', cumulativeTests: cumulative });
+
+  const acc = (rs, k) => (rs.length ? rs.filter((r) => r[k]).length / rs.length : null);
+
+  console.log(JSON.stringify({
+    link: 'LINK 2 (coarse) — pen quality + reliever archetype',
+    team_games_total: all.length,
+    concentrated_subset_elevated_early_exit: subset.length,
+    league_mean_pen_quality: round4(leagueQ),
+    cumulative_tests: cumulative,
+
+    quality_grain: {
+      on_concentrated_subset: gateQuality(subset, leagueQ, cumulative, 'gamePk', 'link2b_pen_quality_subset'),
+      sensitivity_team_clustered: gateQuality(subset, leagueQ, cumulative, 'team', 'link2b_pen_quality_teamclust'),
+      pooled_all_games: gateQuality(all, leagueQ, cumulative, 'gamePk', 'link2b_pen_quality_pooled'),
+    },
+
+    archetype_grain: {
+      n: archRows.length,
+      modal_archetype: modal,
+      baseline_accuracy_guess_modal: round4(acc(archRows.map((r) => ({ x: r.actual_archetype === modal })), 'x')),
+      model_accuracy: round4(acc(archRows.map((r) => ({ x: r.actual_archetype === r.pred_archetype })), 'x')),
+      verdict: archGate,
+    },
+
+    cluster_note: '76% of game pen-quality variance is WITHIN team, so the game is the honest cluster; team-clustered reported as the conservative sensitivity',
+  }, null, 2));
+  process.exit(0);
+})();
+
+const round4 = (v) => (v == null ? null : Math.round(v * 10000) / 10000);
diff --git a/specs/link2-pen-quality.md b/specs/link2-pen-quality.md
new file mode 100644
index 0000000..d074012
--- /dev/null
+++ b/specs/link2-pen-quality.md
@@ -0,0 +1,130 @@
+# Link 2 at the coarse grain — pen QUALITY proves, archetype does not
+
+**The refinement was right.** Naming the individual reliever failed; asking the
+same question at the grain the chain actually needs passes, and the payoff it
+transmits is larger than anything else measured in this chain.
+
+---
+
+## Why this was worth re-asking
+
+Last session closed Link 2 on the individual grain and separately measured that
+the bullpen is, on average, no softer than the starter (+0.0010 on 35,760 plate
+appearances). That null does **not** rule this out, and conflating the two would
+have been an error: an average washing out is entirely consistent with QUALITY
+VARIATION mattering a great deal.
+
+It does. Measured on 18,809 post-starter plate appearances against arms with ≥40
+prior appearances:
+
+| arm faced (prior allowed-hit-rate quartile) | n | realized hit rate |
+|---|---|---|
+| Q1 — best arms | 4,702 | **0.2244** ±0.0119 |
+| Q2 | 4,702 | 0.2293 |
+| Q3 | 4,702 | 0.2410 |
+| Q4 — worst arms | 4,702 | **0.2501** ±0.0124 |
+
+Monotone, spread **+2.57pp** — larger than the whole times-through-the-order
+effect (+1.6pp) and far larger than the pen-vs-starter difference (−0.7pp).
+
+---
+
+## The cluster unit, corrected — and then checked rather than argued
+
+Last session refused Link 2 partly as a team-borne prediction: 30 bullpens, the
+park-geometry ceiling. For QUALITY that argument needed re-testing, and the first
+number I reached for was the wrong one.
+
+- **Treatment variance:** 76% of a game's pen-quality variance is WITHIN team.
+  That says pen quality is not chiefly a club property — but it is a statement
+  about the treatment, not about where ERRORS correlate, and those are different
+  claims. Stopping there would have been picking the convenient answer.
+- **Measured directly:** ICC of the prediction ERROR by team = **0.0261**. With
+  16.7 rows per club that is a design effect of 1.41, inflating standard errors
+  ~19% — small, but not nothing on a marginal interval.
+
+So the verdict was checked under all three treatments rather than resting on the
+most favourable:
+
+| inference treatment | CI on loss delta | |
+|---|---|---|
+| unclustered | [−0.0067, −0.0010] | excludes zero |
+| team-clustered (30 clusters, floor overridden — indicative only) | [−0.0086, −0.0003] | excludes zero |
+| design-effect adjusted (deff 1.41) | [−0.0072, −0.0005] | excludes zero |
+
+It survives all three. Note the team-clustered run sits below this codebase's own
+40-cluster floor and is reported as indicative, not as a pass.
+
+---
+
+## The gate
+
+Concentrated subset as instructed — team-games where Link 1's point-in-time
+early-exit signal is elevated (predicted ≤22 batters faced), i.e. where the pen
+actually enters for the later plate appearances.
+
+### QUALITY grain — **PROVES**
+
+```
+n=501 team-games · 426 game clusters · 110 cumulative tests
+MAE 0.0294 (league-average baseline) -> 0.0260   delta -0.0034
+CI [-0.0063,-0.0005] at 0.9995                   VERDICT: PROVES
+```
+Pooled across all games it also proves (n=1,305, delta −0.0030, CI [−0.0049,
+−0.0015]), so the result is not an artefact of the subset.
+
+### ARCHETYPE grain — **NOT PROVEN**
+
+```
+n=501 · modal-guess baseline 0.5309 -> model 0.5669
+corrected interval [-0.1073, +0.0268] spans zero
+VERDICT: NOT_PROVEN_AT_CORRECTED_BAR
+```
+Two grains were tested; one earned a place. `penQuality.js` deliberately exposes
+no archetype, and a test asserts it.
+
+---
+
+## What Link 3 actually receives
+
+The number that matters is not the MAE gain but how much real outcome separation
+the prediction buys — measured on realized outcomes, prediction strictly
+point-in-time:
+
+| our prediction | games | PAs | realized hit rate |
+|---|---|---|---|
+| predicted BEST pen (bottom tercile) | 167 | 2,044 | **0.2231** ±0.0180 |
+| predicted WORST pen (top tercile) | 167 | 1,799 | **0.2501** ±0.0200 |
+
+**2.70pp of realized separation**, intervals non-overlapping — capturing nearly
+all of the 2.57pp available at the quartile grain. corr(predicted, actual pen
+quality) = 0.393.
+
+Caveat stated rather than buried: the tercile split point is chosen in-sample.
+The prediction driving the separation is point-in-time, so this is a forward
+measurement, but the cut is not.
+
+---
+
+## Built
+
+`src/services/model/penQuality.js` (+ 9 tests) — the proven half, ready for Link
+3. `projectPen` abstains below 5 prior club games; `armQuality` abstains below 40
+appearances. A league-average stand-in would assert "this is an ordinary
+bullpen", which is a claim, and usually the wrong one for exactly the clubs whose
+pens have just turned over.
+
+`hitRateShift` carries the measured consequence, bounded — it was measured over a
+range and is not extrapolated past one.
+
+**Link 3 is now unblocked** on a proven Link 2 at the quality grain only. It is
+not run here; the order scopes this session to building and gating Link 2.
+
+## Parallel track — total_bases per-archetype (logged, not run)
+
+Unchanged from last session: `total_bases` settled n=948 pooled, BOMBER × TB
+**340**, short by 160. Sample-readiness only, not a verdict. The second blocker
+from `specs/per-archetype-grade-bands.md` still applies — the grade does not yet
+separate within any archetype.
+
+Counter and frozen clusters byte-identical.
diff --git a/src/services/model/penQuality.js b/src/services/model/penQuality.js
new file mode 100644
index 0000000..218c450
--- /dev/null
+++ b/src/services/model/penQuality.js
@@ -0,0 +1,101 @@
+'use strict';
+
+/**
+ * penQuality — the PROVEN half of Link 2.
+ *
+ * Link 2 was asked twice. Naming the individual reliever failed on merit (17.2%
+ * accuracy — wrong five times in six), because managers mix and match and the
+ * individual genuinely is noise. Asked at the COARSE grain the chain actually
+ * needs, it proves: predicted pen quality separates a realized 2.70pp hit-rate
+ * difference between the pens we call best and worst.
+ *
+ * The archetype grain did NOT prove (0.567 vs a 0.531 modal-guess baseline,
+ * corrected interval spanning zero) and is deliberately absent from this module.
+ * Two grains were tested; one earned a place.
+ *
+ * ── POINT-IN-TIME ON BOTH SIDES ──────────────────────────────────────────
+ * An arm's quality is his allowed-hit-rate over appearances strictly BEFORE the
+ * game in question, and a club's pen forecast comes only from its prior games.
+ * The target is which KNOWN-quality arms appeared — never how they happened to
+ * pitch that night, which would be scoring against the answer.
+ *
+ * ── ABSTAIN, NEVER IMPUTE ────────────────────────────────────────────────
+ * An arm below the appearance floor has no readable quality, and a club without
+ * enough prior games has no readable pen. Both return null. A league-average
+ * stand-in would assert "this is an ordinary bullpen", which is a claim, and
+ * usually the wrong one for exactly the clubs whose pens have just turned over.
+ */
+
+const { knownNumber } = require('../../utils/known');
+
+/** Appearances before an arm's quality is readable at all. */
+const MIN_ARM_PA = 40;
+/** Prior games before a club's pen is readable at all. */
+const MIN_PRIOR_GAMES = 5;
+
+const mean = (xs) => (xs.length ? xs.reduce((a, b) => a + b, 0) / xs.length : null);
+
+/**
+ * One arm's quality from his prior line. Null below the floor — a 12-batter
+ * sample is not a scouting report.
+ */
+function armQuality(prior) {
+  const n = knownNumber(prior && prior.pa);
+  const h = knownNumber(prior && prior.hits);
+  if (n === null || h === null || n < MIN_ARM_PA) return null;
+  return h / n;
+}
+
+/**
+ * The pen a hitter's later plate appearances will face.
+ *
+ * @param {Array} priorGames  [{ quality }] this club's prior relief outings
+ * @returns {object|null} null when unreadable — never a league-average guess.
+ */
+function projectPen(priorGames) {
+  const qs = (priorGames || []).map((g) => knownNumber(g && g.quality)).filter((v) => v !== null);
+  if (qs.length < MIN_PRIOR_GAMES) {
+    return null;
+  }
+  return {
+    readable: true,
+    quality: round4(mean(qs)),
+    games_read: qs.length,
+    // Stated so a consumer cannot mistake this for a reliever-identity claim.
+    grain: 'pen_quality',
+    individual_arm_refused: 'naming the specific reliever did not prove (17.2% accuracy) — managers mix and match',
+    archetype_refused: 'the archetype grain did not prove at the corrected bar',
+  };
+}
+
+/**
+ * The measured relationship between pen quality and hit rate, for a consumer
+ * that wants the consequence rather than the input. Anchored on the observed
+ * league mean; the slope is the measured tercile separation, not a fitted
+ * parameter, and the effect is bounded because it was measured over a range.
+ */
+const LEAGUE_PEN_QUALITY = 0.2261;
+const HIT_RATE_PER_QUALITY = 0.87;    // 2.70pp realized over a 0.031 quality gap
+const MAX_SHIFT = 0.03;
+
+function hitRateShift(penQuality) {
+  const q = knownNumber(penQuality);
+  if (q === null) return null;         // absent stays absent
+  const raw = (q - LEAGUE_PEN_QUALITY) * HIT_RATE_PER_QUALITY;
+  return round4(Math.max(-MAX_SHIFT, Math.min(MAX_SHIFT, raw)));
+}
+
+/** A checkable sentence, or nothing. */
+function explain(pen) {
+  if (!pen || !pen.readable) return null;
+  const d = pen.quality - LEAGUE_PEN_QUALITY;
+  if (Math.abs(d) < 0.005) return `bullpen reads league-average over ${pen.games_read} prior games`;
+  return `bullpen reads ${d > 0 ? 'weaker' : 'stronger'} than league over ${pen.games_read} prior games`;
+}
+
+const round4 = (v) => (v == null || !Number.isFinite(v) ? null : Math.round(v * 10000) / 10000);
+
+module.exports = {
+  armQuality, projectPen, hitRateShift, explain,
+  MIN_ARM_PA, MIN_PRIOR_GAMES, LEAGUE_PEN_QUALITY, MAX_SHIFT,
+};
diff --git a/tests/unit/penQuality.test.js b/tests/unit/penQuality.test.js
new file mode 100644
index 0000000..4df747a
--- /dev/null
+++ b/tests/unit/penQuality.test.js
@@ -0,0 +1,78 @@
+'use strict';
+
+/**
+ * The proven half of Link 2.
+ *
+ * Two grains were tested. These lock the one that earned a place and the
+ * refusals that keep the other from creeping back in.
+ */
+
+const pq = require('../../src/services/model/penQuality');
+
+const games = (n, q) => Array.from({ length: n }, () => ({ quality: q }));
+
+describe('abstains rather than imputing', () => {
+  it('an arm below the appearance floor has no readable quality', () => {
+    expect(pq.armQuality({ pa: 12, hits: 3 })).toBeNull();
+    expect(pq.armQuality({ pa: 60, hits: 12 })).toBeCloseTo(0.2, 6);
+  });
+
+  it('a club with too few prior games returns null, not a league-average pen', () => {
+    // A league-average stand-in asserts "this is an ordinary bullpen" — a claim,
+    // and usually the wrong one for exactly the clubs whose pens just turned over.
+    expect(pq.projectPen(games(3, 0.22))).toBeNull();
+    expect(pq.projectPen([])).toBeNull();
+    expect(pq.projectPen(null)).toBeNull();
+    expect(pq.projectPen(games(8, 0.22)).readable).toBe(true);
+  });
+
+  it('unreadable quality produces no shift at all', () => {
+    expect(pq.hitRateShift(null)).toBeNull();
+    expect(pq.hitRateShift(undefined)).toBeNull();
+  });
+});
+
+describe('the grain that proved, and the two that did not', () => {
+  it('reports pen QUALITY and names what it refuses', () => {
+    const pen = pq.projectPen(games(10, 0.24));
+    expect(pen.grain).toBe('pen_quality');
+    expect(pen.individual_arm_refused).toMatch(/17.2%/);
+    expect(pen.archetype_refused).toMatch(/did not prove/);
+    // The module must not offer an archetype — that grain did not earn one.
+    expect(pen.archetype).toBeUndefined();
+  });
+});
+
+describe('the shift follows the measured direction', () => {
+  it('a weaker pen raises the hit rate, a stronger one lowers it', () => {
+    expect(pq.hitRateShift(0.26)).toBeGreaterThan(0);
+    expect(pq.hitRateShift(0.19)).toBeLessThan(0);
+    expect(pq.hitRateShift(pq.LEAGUE_PEN_QUALITY)).toBeCloseTo(0, 6);
+  });
+
+  it('is bounded — it was measured over a range, not extrapolated past one', () => {
+    expect(pq.hitRateShift(0.9)).toBeLessThanOrEqual(pq.MAX_SHIFT + 1e-9);
+    expect(pq.hitRateShift(0.01)).toBeGreaterThanOrEqual(-pq.MAX_SHIFT - 1e-9);
+  });
+
+  it('reproduces the measured tercile separation', () => {
+    // Predicted-best pens averaged 0.2114 actual quality and a 0.2231 realized
+    // hit rate; predicted-worst 0.2425 and 0.2501 — a 2.70pp gap.
+    const gap = pq.hitRateShift(0.2425) - pq.hitRateShift(0.2114);
+    expect(gap).toBeGreaterThan(0.02);
+    expect(gap).toBeLessThan(0.035);
+  });
+});
+
+describe('reasoning is true or absent', () => {
+  it('no read means no sentence', () => {
+    expect(pq.explain(null)).toBeNull();
+    expect(pq.explain({ readable: false })).toBeNull();
+  });
+
+  it('names the direction and how much was read', () => {
+    expect(pq.explain(pq.projectPen(games(12, 0.25)))).toMatch(/weaker than league over 12 prior games/);
+    expect(pq.explain(pq.projectPen(games(12, 0.20)))).toMatch(/stronger than league/);
+    expect(pq.explain(pq.projectPen(games(12, 0.2261)))).toMatch(/league-average/);
+  });
+});
diff --git a/web/public/sw.js b/web/public/sw.js
index d0dfa25..a831117 100644
--- a/web/public/sw.js
+++ b/web/public/sw.js
@@ -1,2 +1,2 @@
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