Automated recognition by multiple convolutional neural networks of modern, fossil, intact and damaged pollen grains
- Authors:
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1703361
- Resource Type:
- Publisher's Accepted Manuscript
- Journal Name:
- Computers and Geosciences
- Additional Journal Information:
- Journal Name: Computers and Geosciences Journal Volume: 140 Journal Issue: C; Journal ID: ISSN 0098-3004
- Publisher:
- Elsevier
- Country of Publication:
- United Kingdom
- Language:
- English
Citation Formats
Bourel, Benjamin, Marchant, Ross, de Garidel-Thoron, Thibault, Tetard, Martin, Barboni, Doris, Gally, Yves, and Beaufort, Luc. Automated recognition by multiple convolutional neural networks of modern, fossil, intact and damaged pollen grains. United Kingdom: N. p., 2020.
Web. doi:10.1016/j.cageo.2020.104498.
Bourel, Benjamin, Marchant, Ross, de Garidel-Thoron, Thibault, Tetard, Martin, Barboni, Doris, Gally, Yves, & Beaufort, Luc. Automated recognition by multiple convolutional neural networks of modern, fossil, intact and damaged pollen grains. United Kingdom. https://doi.org/10.1016/j.cageo.2020.104498
Bourel, Benjamin, Marchant, Ross, de Garidel-Thoron, Thibault, Tetard, Martin, Barboni, Doris, Gally, Yves, and Beaufort, Luc. Wed .
"Automated recognition by multiple convolutional neural networks of modern, fossil, intact and damaged pollen grains". United Kingdom. https://doi.org/10.1016/j.cageo.2020.104498.
@article{osti_1703361,
title = {Automated recognition by multiple convolutional neural networks of modern, fossil, intact and damaged pollen grains},
author = {Bourel, Benjamin and Marchant, Ross and de Garidel-Thoron, Thibault and Tetard, Martin and Barboni, Doris and Gally, Yves and Beaufort, Luc},
abstractNote = {},
doi = {10.1016/j.cageo.2020.104498},
journal = {Computers and Geosciences},
number = C,
volume = 140,
place = {United Kingdom},
year = {Wed Jul 01 00:00:00 EDT 2020},
month = {Wed Jul 01 00:00:00 EDT 2020}
}
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https://doi.org/10.1016/j.cageo.2020.104498
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