Fix animation on block text in About IceNet section

Author: bnubald

src/pages/index.astro

@@ -86,43 +86,63 @@ const images = import.meta.glob<{ default: ImageMetadata }>('/src/assets/images/
     </Fragment>
 
     <Fragment slot="subtitle">
-      <p>
-        IceNet is a deep learning sea ice forecasting system developed by an <a
-          class="hyperlink"
-          href="https://www.bas.ac.uk/media-post/artificial-intelligence-to-help-predict-arctic-sea-ice-loss/"
-          >international team and led by the British Antarctic Survey and The Alan Turing Institute</a
-        >. The original IceNet research model, published in <a
-          class="hyperlink"
-          href="https://www.nature.com/articles/s41467-021-25257-4"><b>Nature Communications</b></a
-        >, was trained on climate simulations and observational data to forecast the next 6 months of monthly-averaged
-        sea ice concentration maps. This version advanced the range of accurate sea ice forecasts, outperforming a
-        state-of-the-art dynamical model (ECMWF SEAS5) in seasonal forecasts of summer sea ice, particularly for extreme
-        sea ice events.
-      </p><br />
-      <p>
-        Since then, the IceNet team has focused on building an operational version of the model which forecasts on a
-        daily resolution. The <a class="hyperlink" href="https://www.github.com/tom-andersson/icenet-paper"
-          >original research code</a
-        > was refactored into <code>icenet</code> - <a class="hyperlink" href="https://github.com/icenet-ai/icenet"
-          >a library for operational forecasting</a
-        >. The <code>icenet</code> library can support further research efforts into AI-based operational sea ice forecasting.
-      </p><br />
-      <p>
-        In addition, several use cases and an ecosystem of service components are contained within this organization,
-        supporting execution and downstream analysis.
-      </p>
-      <p>
-        <br />
-        For further information about the team involved, please look at the <a
-          class="hyperlink"
-          href="https://www.bas.ac.uk/project/icenet/">project pages at BAS</a
-        > or <a
-          class="hyperlink"
-          href="https://www.turing.ac.uk/news/artificial-intelligence-help-predict-arctic-sea-ice-loss"
-          >The Alan Turing Institute</a
-        >.
-      </p>
+      <div class="intersect-once intersect-quarter motion-safe:md:opacity-0 motion-safe:md:intersect:animate-fade">
+        <p>
+          IceNet is a deep learning sea ice forecasting system developed by an
+          <a
+            class="hyperlink"
+            href="https://www.bas.ac.uk/media-post/artificial-intelligence-to-help-predict-arctic-sea-ice-loss/"
+          >
+            international team and led by the British Antarctic Survey and The Alan Turing Institute
+          </a>.
+          The original IceNet research model, published in
+          <a
+            class="hyperlink"
+            href="https://www.nature.com/articles/s41467-021-25257-4"
+          >
+            <b>Nature Communications</b>
+          </a>,
+          was trained on climate simulations and observational data to forecast the next 6 months of
+          monthly-averaged sea ice concentration maps. This version advanced the range of accurate sea
+          ice forecasts, outperforming a state-of-the-art dynamical model (ECMWF SEAS5) in seasonal
+          forecasts of summer sea ice, particularly for extreme sea ice events. <br><br>
+        </p>
+
+        <p>
+          Since then, the IceNet team has focused on building an operational version of the model which
+          forecasts on a daily resolution. The
+          <a class="hyperlink" href="https://www.github.com/tom-andersson/icenet-paper">
+            original research code
+          </a>
+          was refactored into <code>icenet</code> –
+          <a class="hyperlink" href="https://github.com/icenet-ai/icenet">
+            a library for operational forecasting
+          </a>.
+          The <code>icenet</code> library can support further research efforts into AI-based operational
+          sea ice forecasting. <br><br>
+        </p>
+
+        <p>
+          In addition, several use cases and an ecosystem of service components are contained within
+          this organisation, supporting execution and downstream analysis.
+        </p>
+
+        <p>
+          For further information about the team involved, please look at the
+          <a class="hyperlink" href="https://www.bas.ac.uk/project/icenet/">
+            project pages at BAS
+          </a>
+          or
+          <a
+            class="hyperlink"
+            href="https://www.turing.ac.uk/news/artificial-intelligence-help-predict-arctic-sea-ice-loss"
+          >
+            The Alan Turing Institute
+          </a>.
+        </p>
+      </div>
     </Fragment>
+
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