rm working paper, added citation

app.py

@@ -799,13 +799,14 @@ with gr.Blocks(theme=theme, css="""
     
     ## Who made this?
 
-    This project was developed by [Maximilian Noichl](https://maxnoichl.eu) (Utrecht University), in cooperation with Andrea Loettgers and Tarja Knuuttila at the [Possible Life project](http://www.possiblelife.eu/), at the University of Vienna. If this project is useful in any way for your research, we would appreciate citation of **...**
+    This project was developed by [Maximilian Noichl](https://maxnoichl.eu) (Utrecht University), in cooperation with Andrea Loettgers and Tarja Knuuttila at the [Possible Life project](http://www.possiblelife.eu/), at the University of Vienna. If this project is useful in any way for your research, we would appreciate citation of:
+     Noichl, M., Loettgers, A., Knuuttila, T. (2025).[Philosophy at Scale: Introducing OpenAlex Mapper](https://maxnoichl.eu/full/talks/talk_BERLIN_April_2025/working_paper.pdf). *Working Paper*.
 
     This project received funding from the European Research Council under the European Union's Horizon 2020 research and innovation programme (LIFEMODE project, grant agreement No. 818772).
 
     ## How does it work?
 
-    The base map for this project is developed by randomly downloading 250,000 articles from OpenAlex, then embedding their abstracts using our [fine-tuned](https://huggingface.co/m7n/discipline-tuned_specter_2_024) version of the [specter-2](https://huggingface.co/allenai/specter2_aug2023refresh_base) language model, running these embeddings through [UMAP](https://umap-learn.readthedocs.io/en/latest/) to give us a two-dimensional representation, and displaying that in an interactive window using [datamapplot](https://datamapplot.readthedocs.io/en/latest/index.html). After the data for your query is downloaded from OpenAlex, it then undergoes the exact same process, but the pre-trained UMAP model from earlier is used to project your new data points onto this original map, showing where they would show up if they were included in the original sample. For more details, you can take a look at the method section of this paper: **...**
+    The base map for this project is developed by randomly downloading 250,000 articles from OpenAlex, then embedding their abstracts using our [fine-tuned](https://huggingface.co/m7n/discipline-tuned_specter_2_024) version of the [specter-2](https://huggingface.co/allenai/specter2_aug2023refresh_base) language model, running these embeddings through [UMAP](https://umap-learn.readthedocs.io/en/latest/) to give us a two-dimensional representation, and displaying that in an interactive window using [datamapplot](https://datamapplot.readthedocs.io/en/latest/index.html). After the data for your query is downloaded from OpenAlex, it then undergoes the exact same process, but the pre-trained UMAP model from earlier is used to project your new data points onto this original map, showing where they would show up if they were included in the original sample. For more details, you can take a look at the method section of this [working paper](https://maxnoichl.eu/full/talks/talk_BERLIN_April_2025/working_paper.pdf).
     
     ## I'm getting an "out of GPU credits" error.