Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Introduction to the Special Track on Artificial Intelligence and COVID-19
Authors: Martin Michalowski, Robert Moskovitch, Nitesh V. Chawla
JAIR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Introduction to the Special Track on Artificial Intelligence and COVID-19. In this special track on Arti๏ฌcial Intelligence and COVID-19, we invited submissions on how data science/AI advancements are addressing challenges related to the global pandemic, including deployments and practical experiences in deploying AI to cope with COVID-19. The response to our call for papers was robust with 24 papers submitted and four accepted. The special track consists of the following papers:... |
| Researcher Affiliation | Academia | Martin Michalowski EMAIL University of Minnesota, Minneapolis MN USA; Robert Moskovitch EMAIL Ben-Gurion University of the Negev, Beersheba Israel; Nitesh V. Chawla EMAIL University of Notre Dame, Notre Dame IN USA |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. It is an introductory paper to a special track and presents an overview of other works. |
| Open Source Code | No | The paper introduces other research papers and does not provide concrete access to source code for its own content or methodology. |
| Open Datasets | No | The paper introduces other research papers and does not use or provide access to its own datasets. |
| Dataset Splits | No | The paper is an introduction to a special track and does not present experimental results using datasets, therefore no dataset splits are provided. |
| Hardware Specification | No | The paper is an introduction to a special track and does not present experimental results, therefore no hardware specifications are provided. |
| Software Dependencies | No | The paper is an introduction to a special track and does not present experimental results, therefore no software dependencies are provided. |
| Experiment Setup | No | The paper is an introduction to a special track and does not present experimental results, therefore no experimental setup details are provided. |