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Machine learning-based global maps of ecological variables and the challenge of assessing them
The recent wave of published global maps of ecological variables has caused as much excitement as it has received criticism. Here we look into the data and methods mostly used for creating these maps, and discuss whether the quality of predicted values can be assessed, globally and locally.
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2022
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9033849/ https://www.ncbi.nlm.nih.gov/pubmed/35459230 http://dx.doi.org/10.1038/s41467-022-29838-9 |
| _version_ | 1784692989782654976 |
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| author | Meyer, Hanna Pebesma, Edzer |
| author_facet | Meyer, Hanna Pebesma, Edzer |
| author_sort | Meyer, Hanna |
| collection | PubMed |
| description | The recent wave of published global maps of ecological variables has caused as much excitement as it has received criticism. Here we look into the data and methods mostly used for creating these maps, and discuss whether the quality of predicted values can be assessed, globally and locally. |
| format | Online Article Text |
| id | pubmed-9033849 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2022 |
| publisher | Nature Publishing Group UK |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-90338492022-04-28 Machine learning-based global maps of ecological variables and the challenge of assessing them Meyer, Hanna Pebesma, Edzer Nat Commun Comment The recent wave of published global maps of ecological variables has caused as much excitement as it has received criticism. Here we look into the data and methods mostly used for creating these maps, and discuss whether the quality of predicted values can be assessed, globally and locally. Nature Publishing Group UK 2022-04-22 /pmc/articles/PMC9033849/ /pubmed/35459230 http://dx.doi.org/10.1038/s41467-022-29838-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
| spellingShingle | Comment Meyer, Hanna Pebesma, Edzer Machine learning-based global maps of ecological variables and the challenge of assessing them |
| title | Machine learning-based global maps of ecological variables and the challenge of assessing them |
| title_full | Machine learning-based global maps of ecological variables and the challenge of assessing them |
| title_fullStr | Machine learning-based global maps of ecological variables and the challenge of assessing them |
| title_full_unstemmed | Machine learning-based global maps of ecological variables and the challenge of assessing them |
| title_short | Machine learning-based global maps of ecological variables and the challenge of assessing them |
| title_sort | machine learning-based global maps of ecological variables and the challenge of assessing them |
| topic | Comment |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9033849/ https://www.ncbi.nlm.nih.gov/pubmed/35459230 http://dx.doi.org/10.1038/s41467-022-29838-9 |
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