AI-Enhanced Language Design - ChatGPT: Bibliometric Analysis and Potential Uses in the Conservation and Restoration of Tropical Ecosystems
DOI:
https://doi.org/10.18800/kawsaypacha.202501.D007Keywords:
ChatGPT, Ecological Restoration, Ecological Conservation, Tropical Ecosystems, Bibliometrics, Technological Interaction.Abstract
ChatGPT, in line with other established artificial intelligence systems, emerges as an advanced tool based on OpenAI's GPT model. This study focuses on exploring its applicability in the conservation and restoration of tropical ecosystems. A bibliometric analysis dissects the main characteristics, learning systems, and potential applications of ChatGPT. Questioned about its groundbreaking potential in the conservation and restoration of tropical ecosystems, ChatGPT outlines applications in assessment and planning, education, monitoring and rapid response, interaction with field tools, and policy support. Although ChatGPT does not inherently provide scientific support, subsequent analysis confirms that its proposals align with current trends in conservation and restoration. When using tools like ChatGPT in scientific contexts, researchers should adopt a critical approach, verifying the provided responses. Additionally, we underscore the need for further research on the ethics of implementing artificial intelligence in conservation and restoration domains.
Downloads
References
Bozkurt, A., Karadeniz, A., Baneres, D., Guerrero-Roldán, A. E., & Rodríguez, M. E. (2021). Artificial Intelligence and Reflections from Educational Landscape: A Review of AI Studies in Half a Century. Sustainability, 13(2), 800. http://dx.doi.org/10.3390/su13020800
Buadze, A., Bhugra, D., & Smith, A. (2023). Generating scholarly content with ChatGPT: Ethical challenges for medical publishing. Lancet Digital Health, 5(3), E105-E106. https://doi.org/10.1016/S2589-7500(23)00019-5
Dimitriadou, E., & Lanitis, A. (2023). A critical evaluation, challenges, and future perspectives of using artificial intelligence and emerging technologies in smart classrooms. Smart Learn. Environ, 10(1), 12. https://doi.org/10.1186/s40561-023-00231-3
Ditria, E. M., Buelow, C. A., Gonzalez-Rivero, M., & Connolly, R. M. (2022). Artificial intelligence and automated monitoring for assisting conservation of marine ecosystems: A perspective. Frontiers in Marine Science, 9. https://doi.org/10.3389/fmars.2022.918104
Else, H. (2023). Abstracts written by ChatGPT fool scientists. Nature 613(7944), 423. https://doi.org/10.1038/d41586-023-00056-7
Fleming, S., Watson, J., Ellenson, A., Cannon, A., & Vesselinov, V. (2021). Machine learning on Earth and environmental science requires education and research policy reforms. Nature Geoscience, 14(12), 878-880.
https://doi.org/10.1038/s41561-021-00865-3
Gao, Y., Skutsch, M., Paneque-Gálvez, J., & Ghilardi, A. (2020). Remote sensing of forest degradation: a review. Environmental Research Letters, 15(10), 103001. https://dx.doi.org/10.1088/1748-9326/abaad7
Haleem, A., Javaid, M., & Singh, R. P. (2022). An era of ChatGPT as a significant futuristic support tool: A study on features, abilities, and challenges. BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 2(4), 100089. https://doi.org/10.1016/j.tbench.2023.100089
Heikkilä, M., & Heaven, W. D. (2022). What’s next for AI: Get a head start with our four big bets for 2023. MIT Technology Review. https://www.technologyreview.com/2022/12/23/1065852/whats-next-for-ai/
Hoekendijk, J. P. A., Kellenberger, B., Aarts, G., et al. (2021). Counting using deep learning regression gives value to ecological surveys. Scientific Reports, 11, 23209. https://doi.org/10.1038/s41598-021-02387-9
Holzinger, A., Keiblinger, K., Holub, P., Zatloukal, K., Müller, H. (2023). AI for life: Trends in artificial intelligence for biotechnology. New Biotechnology, 74, 16-24. https://doi.org/10.1016/j.nbt.2023.02.001
Huang, J., & Tan, M. (2023). The role of ChatGPT in scientific communication: writing better scientific review articles. American journal of cancer research, 13(4). https://pubmed.ncbi.nlm.nih.gov/37168339/
Huh, S. (2023). Issues in the 3rd year of the COVID-19 pandemic, including computer-based testing, study design, ChatGPT, journal metrics, and appreciation to reviewers. Journal of Educational Evaluation for Health Professions, 20(5). https://doi.org/10.3352/jeehp.2023.20.5
Janzen, T. (2023). What are five ways ChatGPT will revolutionize agriculture in the U.S.? https://www.agriculture.com/news/technology/what-are-five-ways-chatgtp-will-revolutionize-agriculture-in-the-us
Kar, A. K., Choudhary, S. K., Singh, V. K. (2022). How can artificial intelligence impact sustainability: A systematic literature review. Journal of Cleaner Production, 376, 134120. https://doi.org/10.1016/j.jclepro.2022.134120
Leorna, S., & Brinkman, T. (2022). Human vs. machine: Detecting wildlife in camera trap images. Ecological Informatics, 72, 101876. https://doi.org/10.1016/j.ecoinf.2022.101876
Liu, J., Shen, L., Wei, G-W. (2023). ChatGPT for Computational Topology. Foundations of Data Science 6(2), 221-250. https://doi.org/ 10.3934/fods.2024009
Meyer, J. G., Urbanowicz, R. J., & Martin, P. C. N. (2023). ChatGPT and large language models in academia: Opportunities and challenges. BioData Mining, 16(20). https://doi.org/10.1186/s13040-023-00339-9
Nunes, J., Cruz, I., & Pinheiro, H. (2020). Speeding up coral reef conservation with AI-aided automated image analysis. Nature Machine Intelligence, 2, 292. https://doi.org/10.1038/s42256-020-0192-3
Patel, J., Manetti, M., Mendelsohn, M., Mills, S., Felden, F., Littig, L., & Rocha, M. (2021). How artificial intelligence can shape policy making. https://www.bcg.com/publications/2021/how-artificial-intelligence-can-shape-policy-making
Pavlik, J. V. (2023). Collaborating With ChatGPT: Considering the Implications of Generative Artificial Intelligence for Journalism and Media Education. Journalism & Mass Communication Educator, 78(1), 84–93. https://doi.org/10.1177/10776958221149577
Pérez, D., González, F., Rodriguez Araujo, M., Paredes, D., & Meinardi, E. (2019). Restoration of Society-Nature Relationship Based on Education: A Model and Progress in Patagonian Drylands. Ecological Restoration, 37, 182-191. https://doi.org/10.3368/er.37.3.182
Ray, P. P. (2023). ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems, 3, 121-154. https://doi.org/10.1016/j.iotcps.2023.04.003
Reyhani Haghighi, S., Pasandideh Saqalaksari, M., & Johnson, S. N. (2023). Artificial Intelligence in Ecology: A Commentary on a Chatbot's Perspective. Bulletin of the Ecological Society of America, 104(4), e02097. https://doi.org/10.1002/bes2.2097
Sadiku, M., Ashaolu, T. J., Ajayi-Majebi, A., & Musa, S. (2021). Artificial Intelligence in Education. International Journal of Scientific Advances, 2(1). https://doi.org/10.51542/ijscia.v2i1.2
Schoormann, T., Strobel, G., Möller, F., Petrik, D., & Zschech, P. (2023). Artificial Intelligence for Sustainability—A Systematic Review of Information Systems Literature. Communications of the Association for Information Systems, 52. https://doi.org/10.17705/1CAIS.05209
Silvestro, D., Goria, S., Sterner, T., & Antonelli, A. (2022). Improving biodiversity protection through artificial intelligence. Nature Sustainability, 5, 1-10. https://doi.org/10.1038/s41893-022-00851-6
Souza-Alonso, P., Saiz, G., García, R. A., Pauchard, A., Ferreira, A., & Merino, A. (2022). Post-fire ecological restoration in Latin American forest ecosystems: Insights and lessons from the last two decades. Forest Ecology and Management, 509, 120083. https://doi.org/10.1016/j.foreco.2022.120083
Stokel-Walker, C., & Van Noorden, R. (2023). What do ChatGPT and generative AI mean for science? Nature, 614(7947), 214–216. https://doi.org/10.1038/d41586-023-00340-6
Swami, N. (2021). Applying AI to conservation challenges. En J. Dunn & P. Balaprakash (Eds.). Data Science Applied to Sustainability Analysis (pp. 17-28). Elsevier. https://doi.org/10.1016/B978-0-12-817976-5.00002-4
Szramowiat-Sala, K. (2023). Artificial Intelligence in Environmental Monitoring: Application of Artificial Neural Networks and Machine Learning for Pollution Prevention and Toxicity Measurements. Preprints https://doi.org/10.20944/preprints202307.1298.v1
Thorp, H. H. (2023). ChatGPT is fun, but not an author. Science, 379(6630), 313. https://doi.org/10.1126/science.adg7879
Tuia, D., Kellenberger, B., Beery, S. (2022). Perspectives in machine learning for wildlife conservation. Nat Commun 13, 792. https://doi.org/10.1038/s41467-022-27980-y
van Dis, E.A.M., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L. (2023). ChatGPT: five priorities for research. Nature, 614(7947), 224-226. https://doi.org/10.1038/d41586-023-00288-7
van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
Whytock, R. C., Suijten, T., van Deursen, T., Świeżewski, J., Mermiaghe, H., Madamba, N., ... Abernethy, K. A. (2021). Real-time alerts from AI-enabled camera traps using the Iridium satellite network: A case-study in Gabon, Central Africa. bioRxiv. https://doi.org/10.1101/2021.11.10.468078
Wu, J., Chen, B., Reynolds, G., Xie, J., O'Brien, M., Liang, S., & Hector, A. (2020). Monitoring tropical forest degradation and restoration with satellite remote sensing: A test using Sabah Biodiversity Experiment. Advances in Ecological Research, 63, 1-30. https://doi.org/10.1016/bs.aecr.2020.01.005
Xue, Z., Xu, C., & Xu, X. (2023). Application of ChatGPT in natural disaster prevention and reduction. Natural Hazards Research, 3, 556-562. https://doi.org/10.1016/j.nhres.2023.07.005
Yadav, M., & Singh, G. (2023). Environmental sustainability with artificial intelligence. EPRA International Journal of Multidisciplinary Research (IJMR), 5, 213-217. https://doi.org/10.36713/epra13325
Yin, X., Li, J., Kadry, S. N., & Sanz-Prieto, I. (2021). Artificial intelligence assisted with intelligent planning framework for environmental restoration of terrestrial ecosystems. Environmental Impact Assessment Review, 86, 106493. https://doi.org/10.1016/j.eiar.2020.106493
Zhu, J., Jiang, J., Yang, M., & Ren, Z. (2023). ChatGPT and Environmental Research. Environmental Science & Technology, 57(46), 17667-17670. https://doi.org/10.1021/acs.est.3c01818
