Applying the maximum entropy principle to neural networks enhances multi‐species distribution models.
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| Title: | Applying the maximum entropy principle to neural networks enhances multi‐species distribution models. |
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| Authors: | Ryckewaert, Maxime1,2 (AUTHOR) maxime.ryckewaert@cirad.fr, Marcos, Diego2 (AUTHOR), Botella, Christophe2 (AUTHOR), Servajean, Maximilien3 (AUTHOR), Bonnet, Pierre4,5 (AUTHOR), Joly, Alexis2 (AUTHOR) |
| Source: | Methods in Ecology & Evolution. May2026, Vol. 17 Issue 5, p1655-1670. 16p. |
| Database: | Environment Complete |
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| Header | DbId: eih DbLabel: Environment Complete An: 193520594 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eih&AN=193520594 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/2041-210x.70262 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 1655 Titles: – TitleFull: Applying the maximum entropy principle to neural networks enhances multi‐species distribution models. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ryckewaert, Maxime – PersonEntity: Name: NameFull: Marcos, Diego – PersonEntity: Name: NameFull: Botella, Christophe – PersonEntity: Name: NameFull: Servajean, Maximilien – PersonEntity: Name: NameFull: Bonnet, Pierre – PersonEntity: Name: NameFull: Joly, Alexis IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 2041210X Numbering: – Type: volume Value: 17 – Type: issue Value: 5 Titles: – TitleFull: Methods in Ecology & Evolution Type: main |
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