View in EDS HTML Full Text PDF Full Text

Machine learning enables reconstruction of past fire regimes from charcoal-derived fire intensity and fuel composition.

Saved in:
Bibliographic Details
Title: Machine learning enables reconstruction of past fire regimes from charcoal-derived fire intensity and fuel composition.
Authors: Kraklow, Vachel1,2 (AUTHOR) vkraklow@lanl.gov, Seitz, Kati3 (AUTHOR), Robbins, Zachary J.1,4 (AUTHOR), Benedict, Katherine B.1 (AUTHOR), Roos, Christopher I.5,6 (AUTHOR), Xu, Chonggang1 (AUTHOR), Dickman, L. Turin1 (AUTHOR), Maezumi, S. Yoshi7 (AUTHOR)
Source: Fire Ecology. 6/11/2026, Vol. 22 Issue 1, p1-23. 23p.
Database: Environment Complete
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: eih
DbLabel: Environment Complete
An: 194517908
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Machine learning enables reconstruction of past fire regimes from charcoal-derived fire intensity and fuel composition.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Kraklow%2C+Vachel%22">Kraklow, Vachel</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> vkraklow@lanl.gov</i><br /><searchLink fieldCode="AR" term="%22Seitz%2C+Kati%22">Seitz, Kati</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Robbins%2C+Zachary+J%2E%22">Robbins, Zachary J.</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Benedict%2C+Katherine+B%2E%22">Benedict, Katherine B.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Roos%2C+Christopher+I%2E%22">Roos, Christopher I.</searchLink><relatesTo>5,6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Chonggang%22">Xu, Chonggang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dickman%2C+L%2E+Turin%22">Dickman, L. Turin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Maezumi%2C+S%2E+Yoshi%22">Maezumi, S. Yoshi</searchLink><relatesTo>7</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Fire+Ecology%22">Fire Ecology</searchLink>. 6/11/2026, Vol. 22 Issue 1, p1-23. 23p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eih&AN=194517908
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1186/s42408-026-00500-9
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 23
        StartPage: 1
    Titles:
      – TitleFull: Machine learning enables reconstruction of past fire regimes from charcoal-derived fire intensity and fuel composition.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Kraklow, Vachel
      – PersonEntity:
          Name:
            NameFull: Seitz, Kati
      – PersonEntity:
          Name:
            NameFull: Robbins, Zachary J.
      – PersonEntity:
          Name:
            NameFull: Benedict, Katherine B.
      – PersonEntity:
          Name:
            NameFull: Roos, Christopher I.
      – PersonEntity:
          Name:
            NameFull: Xu, Chonggang
      – PersonEntity:
          Name:
            NameFull: Dickman, L. Turin
      – PersonEntity:
          Name:
            NameFull: Maezumi, S. Yoshi
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 11
              M: 06
              Text: 6/11/2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19339747
          Numbering:
            – Type: volume
              Value: 22
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Fire Ecology
              Type: main
ResultId 1