update

publications/joss/paper.md

@@ -57,7 +57,7 @@ of space missions will not be fully accomplished. Different analytical and stati
 Bayesian statistics, and packages have been developed [e.g., @batalha2017pandexo; @batalha2019picaso; @MacDonald2023POSEIDON]
 and are utilized in the context of forward- and retrieval-radiative transfer modeling to interpret these datasets and 
 extract crucial information, such as atmospheric temperature, metallicity, carbon-to-oxygen ratio, and surface gravity 
-[e.g., see @Marley2015; @line2014systematic; @Iyer2023Sphinx]. These atmospheric models rely on the synthetic training 
+[e.g., @Marley2015; @line2014systematic; @Iyer2023Sphinx]. These atmospheric models rely on the synthetic training 
 datasets generated by simulating the physics and chemistry of these atmospheres for a wide range of thermal structures 
 and compositions. In addition, different machine learning and deep learning methods have been developed in recent years 
 to perform various tasks, such as confirming the detection of exoplanets [e.g., @Valizadegan2021 by implementing CNN 
@@ -132,6 +132,5 @@ the package to apply to a wider range of telescope datasets.
 Ask the team if they want to acknowledge any grant!!!! 
 Ask Natasha for the Grant!!
 Ask Janet for Gopal's summer support from NASA!!
-E. Gharib-Nezhad expresses gratitude to the developers of many open source Python packages used by `TelescopeML`, ...
 
 # References