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| _creationDate | Datetime | 2024-09-04 9:31:31 PM |
| _modificationDate | Datetime | 2024-11-12 8:52:24 PM |
| _pageID | Integer | 110,965 |
| _pageName | Page | Reference:Reference-025516 |
| _pageTitle | String | Reference-025516
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| Field | Field type | Allowed values | Value |
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| Firstname | String | | Feng Chang, Christina; Astitha, Marina; Yuan, Yongping; Tang, Chunling; Vlahos, Penny; Garcia, Valerie; Khaira, Ummul; |
| BibType | String | journalArticle · report · bookSection · webpage · conferencePaper · MISC · thesis · blogpost · preprint | journalArticle |
| Title | String | | A New Approach to Predict Tributary Phosphorus Loads Using Machine Learning– and Physics-Based Modeling Systems |
| Editors | String | | |
| Year | Date | | July 2023 |
| Journal | String | | Artificial Intelligence for the Earth Systems |
| Booktitle | String | | |
| Volume | String | | 2 |
| Pages | String | | |
| URL | String | | https://journals.ametsoc.org/view/journals/aies/2/3/AIES-D-22-0049.1.xml |
| DOI | String | | 10.1175/AIES-D-22-0049.1 |
| ISBN | String | | |
| Note | String | | Auto downloaded ref at: 2024-09-04 |
1 row is stored for this page
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