Minor typos in JOSS paper

paper.md

@@ -102,7 +102,7 @@ The AI model was developed starting with the TACO dataset, which was available w
 From our experience, the accuracy of the model varies depending on the quality of the images and their context/background. "Trash" is a word people use for an object that lacks purpose, and the purpose of an object is often not obvious in an image. Trash is a nuanced classification because the same object in different settings will not be considered trash (e.g., a drink bottle on someone's desk vs in the forest lying on the ground). This is the main challenge with any image-based trash detection algorithm. Not everything that LOOKS like trash IS trash. This and other complexities to trash classification make a general trash AI a challenging (yet worthwhile) long-term endeavor. The algorithm is primarily trained on the TACO dataset, which is composed of images of single pieces of trash, with the trash lying on the ground (< 1 m away). Thus, model class prediction of trash in these kinds of images will generally be better than trash appearing in aerial images or imaged from a vehicle, for example.
 
 # Availability
-Trash AI is hosted on the web at www.trashai.org. The source code is [available on github](https://github.com/code4sac/trash-ai) with an [MIT license](https://mit-license.org/). The source code can be run offline on any machine that can install [Docker and Docker-compose](www.docker.com). [Documentation](https://github.com/code4sac/trash-ai#trash-ai-web-application-for-serverless-image-classification-of-trash) is maintained by Code for Sacramento and Open Fresno on Github and will be updated with each release. [Nonexhaustive instructions for AWS deployment](https://github.com/code4sac/trash-ai/blob/manuscript/docs/git-aws-account-setup.md) is available for anyone attempting production level deployment.
+Trash AI is hosted on the web at www.trashai.org. The source code is [available on GitHub](https://github.com/code4sac/trash-ai) with an [MIT license](https://mit-license.org/). The source code can be run offline on any machine that can install [Docker and Docker-compose](www.docker.com). [Documentation](https://github.com/code4sac/trash-ai#trash-ai-web-application-for-serverless-image-classification-of-trash) is maintained by Code for Sacramento and Open Fresno on GitHub and will be updated with each release. [Nonexhaustive instructions for AWS deployment](https://github.com/code4sac/trash-ai/blob/manuscript/docs/git-aws-account-setup.md) is available for anyone attempting production level deployment.
 
 # Future Goals
 This workflow is likely to be highly useful for a wide variety of computer vision applications and we hope that people reuse the code for applications beyond trash detection. We aim to increase the labeling of images by creating a user interface that allows users to improve the annotations that the model is currently predicting by manually restructuring the bounding boxes and relabeling the classes. We aim to work in collaboration with the TACO development team to improve our workflow integration to get additional data into the [TACO training dataset](http://tacodataset.org/) by creating an option for users to share their data. Future models will expand the annotations to include the `Trash Taxonomy` [@Hapich:2022] classes and add an option to choose between other models besides the current model.