diff --git a/docs/examples.rst b/docs/examples.rst
index 61853227..96f8d025 100644
--- a/docs/examples.rst
+++ b/docs/examples.rst
@@ -45,6 +45,7 @@ Some comparisons to old versions of pairinteraction, ARC and NIST data.
    examples/comparisons/compare_z_min_cutoff.ipynb
    examples/comparisons/compare_nist_energy_levels_data.ipynb
    examples/comparisons/compare_nist_matrix_elements.ipynb
+   examples/comparisons/compare_udel_portal_matrix_elements.ipynb
    examples/comparisons/compare_model_potentials.ipynb
    examples/comparisons/compare_whittaker.ipynb
 
diff --git a/docs/examples/comparisons/compare_nist_matrix_elements.ipynb b/docs/examples/comparisons/compare_nist_matrix_elements.ipynb
index f9b3ed18..96aff857 100644
--- a/docs/examples/comparisons/compare_nist_matrix_elements.ipynb
+++ b/docs/examples/comparisons/compare_nist_matrix_elements.ipynb
@@ -240,6 +240,74 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Independent check: $5p_j \\rightarrow 6d_{j'}$\n",
+    "\n",
+    "The cutoff $r_c$ was fitted to the principal series $5s_{1/2} \\rightarrow np_j$ only,\n",
+    "so transitions between two *excited* states are an independent test of the dressed operator.\n",
+    "NIST ASD lists three E1 lines connecting the $4p^6\\,5p$ and $4p^6\\,6d$ configurations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        transition       bare    dressed       NIST   (|<5p_j||d||6d_j'>| in e a0)\n",
+      "  5p_1/2 -> 6d_3/2     1.0322     1.0830     1.1798   (ratios to NIST: 0.875 / 0.918)\n",
+      "  5p_3/2 -> 6d_5/2     1.4684     1.5332     1.6580   (ratios to NIST: 0.886 / 0.925)\n",
+      "  5p_3/2 -> 6d_3/2     0.4920     0.5135     0.5577   (ratios to NIST: 0.882 / 0.921)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# {((n, j) lower, (n, j) upper): S in (e a0)^2}\n",
+    "# from https://physics.nist.gov/PhysRefData/ASD/lines_form.html (retrieved 2026-07-28)\n",
+    "nist_s_5p_6d = {\n",
+    "    ((5, 0.5), (6, 1.5)): 1.392,  # 620.63092 nm\n",
+    "    ((5, 1.5), (6, 2.5)): 2.749,  # 629.83252 nm\n",
+    "    ((5, 1.5), (6, 1.5)): 3.11e-1,  # 629.92245 nm\n",
+    "}\n",
+    "\n",
+    "print(f\"{'transition':>18} {'bare':>10} {'dressed':>10} {'NIST':>10}   (|<5p_j||d||6d_j'>| in e a0)\")\n",
+    "for ((n_low, j_low), (n_up, j_up)), line_strength in nist_s_5p_6d.items():\n",
+    "    lower = rydstate.RydbergStateSQDTAlkali(\"Rb\", n_low, l=1, j=j_low)\n",
+    "    upper = rydstate.RydbergStateSQDTAlkali(\"Rb\", n_up, l=2, j=j_up)\n",
+    "    bare = upper.calc_reduced_matrix_element(lower, \"electric_dipole_rydberg\", unit=\"e a0\")\n",
+    "    dressed = upper.calc_reduced_matrix_element(lower, \"electric_dipole\", unit=\"e a0\")\n",
+    "    nist = np.sqrt(line_strength)\n",
+    "    transition = f\"{n_low}p_{2 * j_low:1.0f}/2 -> {n_up}d_{2 * j_up:1.0f}/2\"\n",
+    "    print(\n",
+    "        f\"{transition:>18} {abs(bare):10.4f} {abs(dressed):10.4f} {nist:10.4f}\"\n",
+    "        f\"   (ratios to NIST: {abs(bare) / nist:.3f} / {abs(dressed) / nist:.3f})\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Unlike for the principal series, the shielding correction *increases* these matrix elements, by $4-5\\%$.\n",
+    "The $-\\alpha_c/r^2 (1 - e^{-(r/r_c)^3})$ term contributes only at small $r$ (a few $r_c$), so its sign relative to\n",
+    "the bare integral decides whether it shields or enhances: the $5s \\rightarrow np$ integrals are dominated by a\n",
+    "single sign and are shielded, whereas the $5p \\rightarrow 6d$ integrals suffer a strong cancellation between an\n",
+    "inner positive and an outer negative lobe, and the small-$r$ correction adds to the surviving remainder --- the\n",
+    "same enhancement already seen for the $np \\rightarrow 4d, 5d$ channels above.\n",
+    "\n",
+    "The correction moves all three lines towards NIST, but a deficit of $\\approx 8\\%$ remains.\n",
+    "That deficit is a property of the *reference*, not of the operator: the NIST ASD line strengths for these three\n",
+    "lines are about 10% larger than the high-precision values tabulated by the UD atomic data portal,\n",
+    "far outside the portal's stated uncertainty. Measured against the portal, the dressed operator agrees to\n",
+    "$\\approx 1.5\\%$ --- see the direct comparison in [compare_udel_portal_matrix_elements.ipynb](compare_udel_portal_matrix_elements.ipynb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -255,7 +323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -306,7 +374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-07-27T11:05:51.131147Z",
@@ -373,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -400,7 +468,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
diff --git a/docs/examples/comparisons/compare_udel_portal_matrix_elements.ipynb b/docs/examples/comparisons/compare_udel_portal_matrix_elements.ipynb
new file mode 100644
index 00000000..aaa82983
--- /dev/null
+++ b/docs/examples/comparisons/compare_udel_portal_matrix_elements.ipynb
@@ -0,0 +1,426 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Compare alkali matrix elements with the UD atomic data portal\n",
+    "\n",
+    "The [Portal for High-Precision Atomic Data and Computation](https://www1.udel.edu/atom/) (University of Delaware)\n",
+    "tabulates *reduced* E1 matrix elements directly in $e a_0$, each with an uncertainty,\n",
+    "computed with a linearized coupled-cluster (all-order) method.\n",
+    "\n",
+    "Compared to the NIST ASD line strengths used in\n",
+    "[compare_nist_matrix_elements.ipynb](compare_nist_matrix_elements.ipynb),\n",
+    "this covers far more transitions and is much more accurate for the weak ones.\n",
+    "It therefore lets us test the core-polarization corrected (\"dressed\") electric-dipole operator\n",
+    "\n",
+    "$$d(r) = r - \\frac{\\alpha_c}{r^2}\\left(1 - e^{-(r/r_c)^3}\\right)$$\n",
+    "\n",
+    "on all five alkalis at once, and on transition series its cutoff $r_c$ was never fitted to."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import csv\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "import rydstate\n",
+    "from rydstate.species.element_properties import get_element_properties"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The reference data\n",
+    "\n",
+    "`udel_atom_portal_e1_matrix_elements.csv` next to this notebook holds the alkali subset retrieved from the portal:\n",
+    "3057 unique transitions for Li, Na, K, Rb and Cs, covering the $s$-$p$, $p$-$d$ and $d$-$f$ series\n",
+    "with principal quantum numbers up to $n \\approx 14$.\n",
+    "\n",
+    "The dressed operator is used automatically if `\"electric_dipole\"` is specified.\n",
+    "To compute the bare values for comparison, one can use `\"electric_dipole_rydberg\"` instead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2636 transitions with a portal uncertainty below 5%\n",
+      "  Li  alpha_cs_core =  0.1923, r_c = 3.04   s-p:  152 p-d:  216 d-f:  168\n",
+      "  Na  alpha_cs_core =  0.9448, r_c = 3.18   s-p:  162 p-d:  243 d-f:  216\n",
+      "  K   alpha_cs_core =  5.3310, r_c = 2.30   s-p:  148 p-d:  133 d-f:  183\n",
+      "  Rb  alpha_cs_core =  9.0760, r_c = 3.12   s-p:  148 p-d:  191 d-f:  159\n",
+      "  Cs  alpha_cs_core = 15.6440, r_c = 3.42   s-p:  148 p-d:  211 d-f:  158\n"
+     ]
+    }
+   ],
+   "source": [
+    "portal_file = Path(\"udel_atom_portal_e1_matrix_elements.csv\")\n",
+    "with portal_file.open() as f:\n",
+    "    portal_rows = list(csv.DictReader(line for line in f if not line.startswith(\"#\")))\n",
+    "\n",
+    "max_relative_uncertainty = 0.05  # ignore reference values that are themselves uncertain\n",
+    "\n",
+    "portal = {key: [] for key in [\"species\", \"series\", \"n_max\", \"reference\", \"bare\", \"dressed\"]}\n",
+    "for row in portal_rows:\n",
+    "    reference, uncertainty = float(row[\"matrix_element\"]), float(row[\"uncertainty\"])\n",
+    "    if uncertainty / reference > max_relative_uncertainty:\n",
+    "        continue\n",
+    "    state_1 = rydstate.RydbergStateSQDTAlkali(row[\"species\"], int(row[\"n1\"]), l=int(row[\"l1\"]), j=float(row[\"j1\"]))\n",
+    "    state_2 = rydstate.RydbergStateSQDTAlkali(row[\"species\"], int(row[\"n2\"]), l=int(row[\"l2\"]), j=float(row[\"j2\"]))\n",
+    "    l_low, l_high = sorted([int(row[\"l1\"]), int(row[\"l2\"])])\n",
+    "    portal[\"species\"].append(row[\"species\"])\n",
+    "    portal[\"series\"].append(f\"{'spdf'[l_low]}-{'spdf'[l_high]}\")\n",
+    "    portal[\"n_max\"].append(max(int(row[\"n1\"]), int(row[\"n2\"])))\n",
+    "    portal[\"reference\"].append(reference)\n",
+    "    portal[\"bare\"].append(abs(state_1.calc_reduced_matrix_element(state_2, \"electric_dipole_rydberg\", unit=\"e a0\")))\n",
+    "    portal[\"dressed\"].append(abs(state_1.calc_reduced_matrix_element(state_2, \"electric_dipole\", unit=\"e a0\")))\n",
+    "\n",
+    "portal = {key: np.array(value) for key, value in portal.items()}\n",
+    "ratio = {operator: portal[operator] / portal[\"reference\"] for operator in (\"bare\", \"dressed\")}\n",
+    "# relative size of the core-polarization correction for each transition\n",
+    "correction = np.abs(portal[\"dressed\"] / portal[\"bare\"] - 1)\n",
+    "\n",
+    "species_list = [\"Li\", \"Na\", \"K\", \"Rb\", \"Cs\"]\n",
+    "series_list = [\"s-p\", \"p-d\", \"d-f\"]\n",
+    "print(f\"{len(portal['reference'])} transitions with a portal uncertainty below {max_relative_uncertainty:.0%}\")\n",
+    "for species in species_list:\n",
+    "    element_properties = get_element_properties(species)\n",
+    "    counts = \" \".join(\n",
+    "        f\"{series}: {np.sum((portal['species'] == species) & (portal['series'] == series)):4d}\"\n",
+    "        for series in series_list\n",
+    "    )\n",
+    "    print(\n",
+    "        f\"  {species:2s}  alpha_cs_core = {element_properties.alpha_closed_shell_core:7.4f},\"\n",
+    "        f\" r_c = {element_properties.r_c_dipole_operator:.2f}   {counts}\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## NIST ASD vs. the portal\n",
+    "\n",
+    "The three Rb $5p \\rightarrow 6d$ lines compared against NIST ASD in\n",
+    "[compare_nist_matrix_elements.ipynb](compare_nist_matrix_elements.ipynb) are also in the portal data,\n",
+    "which lets us check the two references against each other."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        transition   NIST ASD            portal       bare    dressed\n",
+      "  5p_1/2 -> 6d_3/2     1.1798     1.0674 +- 0.0372     1.0322     1.0830\n",
+      "  5p_3/2 -> 6d_3/2     0.5577     0.5064 +- 0.0167     0.4920     0.5135\n",
+      "  5p_3/2 -> 6d_5/2     1.6580     1.5116 +- 0.0484     1.4684     1.5332\n"
+     ]
+    }
+   ],
+   "source": [
+    "# {((n, j) lower, (n, j) upper): S in (e a0)^2} from NIST ASD, see compare_nist_matrix_elements.ipynb\n",
+    "nist_s_5p_6d = {\n",
+    "    ((5, 0.5), (6, 1.5)): 1.392,\n",
+    "    ((5, 1.5), (6, 2.5)): 2.749,\n",
+    "    ((5, 1.5), (6, 1.5)): 3.11e-1,\n",
+    "}\n",
+    "\n",
+    "print(f\"{'transition':>18} {'NIST ASD':>10} {'portal':>17} {'bare':>10} {'dressed':>10}\")\n",
+    "for row in portal_rows:\n",
+    "    states = {(int(row[\"n1\"]), int(row[\"l1\"])), (int(row[\"n2\"]), int(row[\"l2\"]))}\n",
+    "    if row[\"species\"] != \"Rb\" or states != {(5, 1), (6, 2)}:\n",
+    "        continue\n",
+    "    j_low, j_up = float(row[\"j1\"]), float(row[\"j2\"])\n",
+    "    state_low = rydstate.RydbergStateSQDTAlkali(\"Rb\", 5, l=1, j=j_low)\n",
+    "    state_up = rydstate.RydbergStateSQDTAlkali(\"Rb\", 6, l=2, j=j_up)\n",
+    "    transition = f\"5p_{2 * j_low:1.0f}/2 -> 6d_{2 * j_up:1.0f}/2\"\n",
+    "    print(\n",
+    "        f\"{transition:>18} {np.sqrt(nist_s_5p_6d[((5, j_low), (6, j_up))]):10.4f}\"\n",
+    "        f\" {float(row['matrix_element']):10.4f} +- {float(row['uncertainty']):.4f}\"\n",
+    "        f\" {abs(state_up.calc_reduced_matrix_element(state_low, 'electric_dipole_rydberg', unit='e a0')):10.4f}\"\n",
+    "        f\" {abs(state_up.calc_reduced_matrix_element(state_low, 'electric_dipole', unit='e a0')):10.4f}\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The correction only acts where it should\n",
+    "\n",
+    "The core-polarization term matters only when the bare radial integral suffers a strong cancellation.\n",
+    "Grouping the transitions by how much the dressed operator changes the matrix element therefore separates\n",
+    "the transitions the correction is irrelevant for from the ones that actually test it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " size of correction     N   median (and worst) |calc / portal - 1|\n",
+      "                                        bare          dressed\n",
+      "            0% - 1%  2265      0.16% ( 6.4%)   0.15% ( 6.4%)\n",
+      "            1% - 5%   294      1.83% ( 6.6%)   0.89% ( 5.0%)\n",
+      "           5% - 20%    70      7.87% (25.6%)   2.65% ( 8.1%)\n",
+      "          above 20%     7     21.10% (46.0%)   4.52% (10.6%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "correction_bins = [(0.0, 0.01), (0.01, 0.05), (0.05, 0.20), (0.20, np.inf)]\n",
+    "\n",
+    "print(f\"{'size of correction':>19} {'N':>5}   median (and worst) |calc / portal - 1|\")\n",
+    "print(f\"{'':>19} {'':>5}   {'bare':>16} {'dressed':>16}\")\n",
+    "for low, high in correction_bins:\n",
+    "    mask = (correction >= low) & (correction < high)\n",
+    "    label = f\"{low:.0%} - {high:.0%}\" if np.isfinite(high) else f\"above {low:.0%}\"\n",
+    "    deviations = [np.abs(ratio[operator][mask] - 1) for operator in (\"bare\", \"dressed\")]\n",
+    "    columns = \"\".join(f\"{np.median(d):8.2%} ({np.max(d):5.1%})\" for d in deviations)\n",
+    "    print(f\"{label:>19} {mask.sum():5d}   {columns}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(7.5, 5))\n",
+    "\n",
+    "styles = {\"bare\": (\"C0\", \"o\"), \"dressed\": (\"C1\", \"s\")}\n",
+    "for operator, (color, marker) in styles.items():\n",
+    "    ax.plot(correction, ratio[operator], marker, color=color, markersize=4, linestyle=\"none\", alpha=0.5, label=operator)\n",
+    "\n",
+    "ax.axhline(1, color=\"gray\", lw=0.8, zorder=-10)\n",
+    "ax.set_xscale(\"log\")\n",
+    "ax.set_xlim(1e-3, 1)  # for the 1550 transitions with a smaller correction both operators agree to 0.1% (median)\n",
+    "ax.set_ylim(0.85, 1.5)\n",
+    "ax.set_xlabel(r\"size of the core-polarization correction  $|d_\\mathrm{dressed} / d_\\mathrm{bare} - 1|$\")\n",
+    "ax.set_ylabel(r\"$|\\langle a \\Vert d \\Vert b \\rangle|$ calculated / portal\")\n",
+    "ax.legend()\n",
+    "\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ratio per species and transition series\n",
+    "\n",
+    "The aggregate view above hides which transitions the deviations belong to.\n",
+    "The plots below therefore show the ratio calculated / portal for each species separately,\n",
+    "with the three transition series colour coded and plotted against the principal quantum number\n",
+    "of the more excited state of each transition."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x650 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(2, len(species_list), figsize=(15, 6.5), sharex=True, sharey=True)\n",
+    "\n",
+    "series_styles = {\"s-p\": (\"C0\", \"o\"), \"p-d\": (\"C1\", \"s\"), \"d-f\": (\"C4\", \"^\")}\n",
+    "series_labels = {\n",
+    "    \"s-p\": r\"$ns \\leftrightarrow n'p$\",\n",
+    "    \"p-d\": r\"$np \\leftrightarrow n'd$\",\n",
+    "    \"d-f\": r\"$nd \\leftrightarrow n'f$\",\n",
+    "}\n",
+    "\n",
+    "for column, species in enumerate(species_list):\n",
+    "    for row, operator in enumerate(styles):\n",
+    "        ax = axes[row, column]\n",
+    "        for series, (color, marker) in series_styles.items():\n",
+    "            mask = (portal[\"species\"] == species) & (portal[\"series\"] == series)\n",
+    "            ax.plot(\n",
+    "                portal[\"n_max\"][mask],\n",
+    "                ratio[operator][mask],\n",
+    "                marker,\n",
+    "                color=color,\n",
+    "                markersize=4,\n",
+    "                linestyle=\"none\",\n",
+    "                alpha=0.6,\n",
+    "                label=series_labels[series],\n",
+    "            )\n",
+    "        ax.axhline(1, color=\"gray\", lw=0.8, zorder=-10)\n",
+    "\n",
+    "for column, species in enumerate(species_list):\n",
+    "    alpha = get_element_properties(species).alpha_closed_shell_core\n",
+    "    axes[0, column].set_title(rf\"{species}   ($\\alpha_c$ = {alpha:.2f})\")\n",
+    "    axes[-1, column].set_xlabel(r\"$n$ of the more excited state\")\n",
+    "    axes[-1, column].set_xticks(range(2, 15, 3))\n",
+    "for row, operator in enumerate(styles):\n",
+    "    axes[row, 0].set_ylabel(f\"{operator}\\ncalculated / portal\")\n",
+    "\n",
+    "axes[0, 0].set_ylim(0.85, 1.5)\n",
+    "axes[0, 0].legend()\n",
+    "\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What the panels show:\n",
+    "\n",
+    "- **Li** ($\\alpha_c = 0.19$): the correction is invisible on this scale, as it should be.\n",
+    "- The branch of $ns \\leftrightarrow n'p$ points drifting upwards with $n$ in the bare row is in each case the\n",
+    "  principal series from the ground state, whose radial integral cancels more and more strongly with increasing $n$.\n",
+    "  This is where the bare operator fails worst (up to $+46\\%$ for Cs), and the dressed operator removes most of it.\n",
+    "- The $np \\leftrightarrow n'd$ and $nd \\leftrightarrow n'f$ series stay within a few percent for both operators ---\n",
+    "  these states barely penetrate the core.\n",
+    "- A residual remains for the Na principal series ($+8\\%$ at $n = 11$). Its $r_c$ was fitted to the NIST ASD line\n",
+    "  strengths of that very series, which are themselves less accurate than the portal values;\n",
+    "  a smaller $r_c \\approx 2.4$ would reproduce the portal data for Na to about 2%.\n",
+    "- The two outliers at $n = 14$ in the dressed row for Cs are the very weak $5d_{3/2} \\leftrightarrow np_{1/2}$ lines,\n",
+    "  where the integral almost vanishes, the portal uncertainty is itself 5%, and the correction overshoots."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Per species\n",
+    "\n",
+    "The fitted $r_c$ values were obtained from the principal series ($s$-$p$) of each species,\n",
+    "so the $p$-$d$ and $d$-$f$ transitions are an independent test.\n",
+    "Below we restrict to the transitions where the correction exceeds 1%, i.e. those that actually probe it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x450 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 4.5), sharey=True)\n",
+    "subsets = {\n",
+    "    \"all series\": correction > 0.01,\n",
+    "    \"$p$-$d$ and $d$-$f$ only (independent of the fit)\": (correction > 0.01) & (portal[\"series\"] != \"s-p\"),\n",
+    "}\n",
+    "\n",
+    "offsets = {\"bare\": -0.19, \"dressed\": +0.19}\n",
+    "for ax, (title, subset) in zip(axes, subsets.items(), strict=True):\n",
+    "    labels = []\n",
+    "    for i, species in enumerate(species_list):\n",
+    "        mask = subset & (portal[\"species\"] == species)\n",
+    "        labels.append(f\"{species}\\n({mask.sum()})\")\n",
+    "        for operator, (color, _marker) in styles.items():\n",
+    "            if not mask.any():\n",
+    "                continue\n",
+    "            median = np.median(np.abs(ratio[operator][mask] - 1))\n",
+    "            ax.bar(i + offsets[operator], median, width=0.36, color=color, label=operator if i == 0 else None)\n",
+    "\n",
+    "    ax.set_xticks(range(len(species_list)), labels=labels)\n",
+    "    ax.set_title(title, fontsize=10)\n",
+    "    ax.set_yscale(\"log\")\n",
+    "\n",
+    "axes[0].set_ylabel(r\"median $|$calc / portal $- 1|$\")\n",
+    "axes[0].set_xlabel(\"species (number of transitions)\")\n",
+    "axes[1].set_xlabel(\"species (number of transitions)\")\n",
+    "axes[0].legend()\n",
+    "\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wherever the core-polarization correction is relevant it moves the matrix elements towards the\n",
+    "high-precision reference, for every alkali and for series the cutoff radius was never fitted to.\n",
+    "The improvement is largest exactly where the bare operator fails worst: for the handful of transitions where the\n",
+    "correction exceeds 20% --- the $ns \\rightarrow (n+1)p$ lines of K, Rb and Cs, whose radial integrals nearly cancel ---\n",
+    "the bare operator is off by up to 46%, the dressed one by at most about 10%.\n",
+    "\n",
+    "For the vast majority of transitions the correction is below 1% and changes nothing;\n",
+    "in particular it never degrades the transitions it does not apply to."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "rydstate",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/examples/comparisons/udel_atom_portal_e1_matrix_elements.csv b/docs/examples/comparisons/udel_atom_portal_e1_matrix_elements.csv
new file mode 100644
index 00000000..49a1b838
--- /dev/null
+++ b/docs/examples/comparisons/udel_atom_portal_e1_matrix_elements.csv
@@ -0,0 +1,3061 @@
+# Reduced electric-dipole matrix elements |<n1 l1 j1 || d || n2 l2 j2>| in e a0,
+# from the Portal for High-Precision Atomic Data and Computation, https://www1.udel.edu/atom/
+# (M. S. Safronova et al., linearized coupled-cluster method; retrieved 2026-07-28).
+species,n1,l1,j1,n2,l2,j2,matrix_element,uncertainty
+Li,2,0,0.5,2,1,0.5,3.317,0.0004
+Li,2,0,0.5,2,1,1.5,4.6907,0.0006
+Li,2,0,0.5,3,1,0.5,0.182926,0.0001662
+Li,2,0,0.5,3,1,1.5,0.2587,0.000296
+Li,2,0,0.5,4,1,0.5,0.160101,8.01e-05
+Li,2,0,0.5,4,1,1.5,0.22642,0.0001321
+Li,2,0,0.5,5,1,0.5,0.119748,5.99e-05
+Li,2,0,0.5,5,1,1.5,0.169352,8.47e-05
+Li,2,0,0.5,6,1,0.5,0.0925072,0.0002975
+Li,2,0,0.5,6,1,1.5,0.130826,0.0004344
+Li,2,0,0.5,7,1,0.5,0.0736349,0.00015
+Li,2,0,0.5,7,1,1.5,0.104137,0.0002226
+Li,2,0,0.5,8,1,0.5,0.0610664,0.0008243
+Li,2,0,0.5,8,1,1.5,0.086362,0.0011741
+Li,2,0,0.5,9,1,0.5,0.0503411,0.0001746
+Li,2,0,0.5,9,1,1.5,0.0711939,0.0002402
+Li,2,0,0.5,10,1,0.5,0.04354,0.0004045
+Li,2,0,0.5,10,1,1.5,0.0615756,0.0005778
+Li,2,1,0.5,3,2,1.5,5.06647,0.0025332
+Li,2,1,0.5,4,2,1.5,1.92912,0.0009646
+Li,2,1,0.5,5,2,1.5,1.12137,0.0005607
+Li,2,1,0.5,6,2,1.5,0.768177,0.0006592
+Li,2,1,0.5,7,2,1.5,0.573644,0.0002868
+Li,2,1,0.5,8,2,1.5,0.451138,0.0008895
+Li,2,1,0.5,9,2,1.5,0.367907,0.0013707
+Li,2,1,0.5,10,2,1.5,0.308399,0.0011338
+Li,2,1,1.5,3,2,1.5,2.26584,0.0011329
+Li,2,1,1.5,3,2,2.5,6.79752,0.0033988
+Li,2,1,1.5,4,2,1.5,0.862732,0.0004314
+Li,2,1,1.5,4,2,2.5,2.58822,0.0012941
+Li,2,1,1.5,5,2,1.5,0.501491,0.0002507
+Li,2,1,1.5,5,2,2.5,1.50449,0.0007522
+Li,2,1,1.5,6,2,1.5,0.343537,0.0002947
+Li,2,1,1.5,6,2,2.5,1.03062,0.0008842
+Li,2,1,1.5,7,2,1.5,0.256539,0.0001283
+Li,2,1,1.5,7,2,2.5,0.76963,0.0003848
+Li,2,1,1.5,8,2,1.5,0.201753,0.0003979
+Li,2,1,1.5,8,2,2.5,0.605265,0.0011998
+Li,2,1,1.5,9,2,1.5,0.164531,0.0006131
+Li,2,1,1.5,9,2,2.5,0.493598,0.0018445
+Li,2,1,1.5,10,2,1.5,0.137919,0.0005071
+Li,2,1,1.5,10,2,2.5,0.41376,0.001526
+Li,3,2,1.5,4,3,2.5,15.8114,0.0079057
+Li,3,2,1.5,5,3,2.5,5.14129,0.0410153
+Li,3,2,1.5,6,3,2.5,2.78785,0.0013939
+Li,3,2,1.5,7,3,2.5,1.84195,0.000921
+Li,3,2,1.5,8,3,2.5,1.348,0.000674
+Li,3,2,1.5,9,3,2.5,1.04926,0.0005246
+Li,3,2,1.5,10,3,2.5,0.850912,0.0004255
+Li,3,2,2.5,4,3,2.5,4.22581,0.0021129
+Li,3,2,2.5,4,3,3.5,18.8984,0.0094492
+Li,3,2,2.5,5,3,2.5,1.37406,0.0109617
+Li,3,2,2.5,5,3,3.5,6.14502,0.0489971
+Li,3,2,2.5,6,3,2.5,0.745078,0.0003725
+Li,3,2,2.5,6,3,3.5,3.33212,0.0016661
+Li,3,2,2.5,7,3,2.5,0.492278,0.0002461
+Li,3,2,2.5,7,3,3.5,2.20155,0.0011008
+Li,3,2,2.5,8,3,2.5,0.360264,0.0001801
+Li,3,2,2.5,8,3,3.5,1.61117,0.0008056
+Li,3,2,2.5,9,3,2.5,0.280424,0.0001402
+Li,3,2,2.5,9,3,3.5,1.25411,0.0006271
+Li,3,2,2.5,10,3,2.5,0.227413,0.0001137
+Li,3,2,2.5,10,3,3.5,1.01703,0.0005085
+Li,2,1,0.5,3,0,0.5,2.43256,0.0012163
+Li,2,1,1.5,3,0,0.5,3.44027,0.0017201
+Li,3,0,0.5,3,1,0.5,8.46745,0.0042337
+Li,3,0,0.5,3,1,1.5,11.9748,0.0059874
+Li,3,0,0.5,4,1,0.5,0.0318905,0.0005758
+Li,3,0,0.5,4,1,1.5,0.0451005,0.0009358
+Li,3,0,0.5,5,1,0.5,0.154393,0.0001874
+Li,3,0,0.5,5,1,1.5,0.218346,0.0003261
+Li,3,0,0.5,6,1,0.5,0.137472,0.0009412
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