// Fuzzy-Score: Levenshtein + Contain-Boost
def similarity = { a, b ->
  def na = normalizeText(a)
  def nb = normalizeText(b)

  // Exakter Teilstring → mind. 90 %
  boolean boost =
    (" "+na+" ").contains(" "+nb+" ")

  int dist = levenshtein(na, nb)
  double base =
    1d - (dist / max(na, nb))
  if (boost) base = max(base, 0.90d)
  min(1d, max(0d, base))
}

// Entscheidung mit Fallback
if (bestScore >= MIN_SCORE) {
  chosen = best        // Treffer
} else if (dummyMV) {
  chosen = dummyMV     // Fallback
} else {
  return               // übersprungen
}