Diseño de lenguaje mejorado con IA -ChatGPT: análisis bibliométrico y usos potenciales en la conservación y restauración de ecosistemas tropicales

  • Smith Ervin Reyes Palomino Universidad Tecnológica del Perú
Palabras clave: ChatGPT, Restauración ecológica, Conservación ecológica, Ecosistemas tropicales, Bibliometría, Interacción tecnológica

Resumen

ChatGPT, en línea con otros sistemas de inteligencia artificial consolidados, surge como una herramienta avanzada basada en el modelo GPT de OpenAI. Este estudio se centra en explorar su aplicabilidad en la conservación y restauración de ecosistemas tropicales. Un análisis bibliométrico examina las principales características, sistemas de aprendizaje y aplicaciones potenciales de ChatGPT. Ante la pregunta sobre su potencial innovador en la conservación y restauración de ecosistemas tropicales, ChatGPT propone aplicaciones en evaluación y planificación, educación, monitoreo y respuesta rápida, interacción con herramientas de campo y apoyo a políticas. Aunque ChatGPT no proporciona respaldo científico de manera inherente, un análisis posterior confirma que sus propuestas se alinean con las tendencias actuales en conservación y restauración. Al utilizar herramientas como ChatGPT en contextos científicos, los investigadores deben adoptar un enfoque crítico, verificando las respuestas proporcionadas. Además, se resalta la necesidad de profundizar en la investigación sobre la ética de la implementación de la inteligencia artificial en los ámbitos de la conservación y la restauración. 

Referencias bibliográficas

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

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Cómo citar
Reyes Palomino, S. (2025). Diseño de lenguaje mejorado con IA -ChatGPT: análisis bibliométrico y usos potenciales en la conservación y restauración de ecosistemas tropicales. Revista Kawsaypacha: Sociedad Y Medio Ambiente, (15), D-007. https://doi.org/10.18800/kawsaypacha.202501.D007