Zobrazeno 1 - 10
of 18
pro vyhledávání: '"JIN-SI R. OVER"'
Autor:
Evan B. Goldstein, Daniel Buscombe, Eli D. Lazarus, Somya D. Mohanty, Shah Nafis Rafique, Katherine A. Anarde, Andrew D. Ashton, Tomas Beuzen, Katherine A. Castagno, Nicholas Cohn, Matthew P. Conlin, Ashley Ellenson, Megan Gillen, Paige A. Hovenga, Jin‐Si R. Over, Rose V. Palermo, Katherine M. Ratliff, Ian R. B. Reeves, Lily H. Sanborn, Jessamin A. Straub, Luke A. Taylor, Elizabeth J. Wallace, Jonathan Warrick, Phillipe Wernette, Hannah E. Williams
Publikováno v:
Earth and Space Science, Vol 8, Iss 9, Pp n/a-n/a (2021)
Abstract Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for example, associated with each image. Data‐driven models are only as good as the data used for training, and this
Externí odkaz:
https://doaj.org/article/6ffcb5c803fd4ffe94cf5547a5b77f7a
Autor:
ALFREDO L. ARETXABALETA, CHRISTOPHER R. SHERWOOD, BRIAN O. BLANTON, JIN-SI R. OVER, PETER A. TRAYKOVSKI, ERDINC SOGUT
Publikováno v:
Coastal Sediments 2023.
Autor:
MICHAEL ITZKIN, MARGARET L. PALMSTEN, MARK L. BUCKLEY, CHRISTOPHER R. SHERWOOD, JENNA A. BROWN, JIN-SI R. OVER, PETER TRAYKOVSKI
Publikováno v:
Coastal Sediments 2023.
Autor:
CHRISTOPHER R. SHERWOOD, ALFREDO L. ARETXABALETA, PETER TRAYKOVSKI, JIN-SI R. OVER, ERIN LYONS, DAVID S. FOSTER, JENNIFER L. MISELIS, TIMOTHY R. NELSON, ERDINC SOGUT
Publikováno v:
Coastal Sediments 2023.
Autor:
Christopher Sherwood, Christopher R Sherwood, Andrew C Ritchie, Jin-Si R Over, Christine J Kranenburg, Jonathan A Warrick, Jenna A Brown, C Wayne Wright, Alfredo L Aretxabaleta, Sara L Zeigler, Phillipe A Wernette, Daniel D Buscombe, Christie A Hegermiller
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::53a9ce7cabc27f19702e8538402a1699
https://doi.org/10.22541/essoar.167388282.28584763/v1
https://doi.org/10.22541/essoar.167388282.28584763/v1
Autor:
Christopher R. Sherwood, Andrew C. Ritchie, Jin‐Si R. Over, Christine J. Kranenburg, Jonathan A. Warrick, Jenna A. Brown, C. Wayne Wright, Alfredo L. Aretxabaleta, Sara L. Zeigler, Phillipe A. Wernette, Daniel D. Buscombe, Christie A. Hegermiller
Publikováno v:
Journal of Geophysical Research: Earth Surface. 128
Autor:
Christopher R. Sherwood, Andy Ritchie, Jin-Si R Over, Christine J Kranenburg, Jonathan A Warrick, Jenna A. Brown, C. Wayne Wright, Alfredo L. Aretxabaleta, Sara Zeigler, Phillipe Alan Wernette, Daniel Buscombe, Christie A Hegermiller
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e271403892c14646ec1e5add00be3f04
https://doi.org/10.1002/essoar.10512457.1
https://doi.org/10.1002/essoar.10512457.1
Autor:
Phillipe A. Wernette, Jenna Brown, Andrew C. Ritchie, Jonathan A. Warrick, Christie A. Hegermiller, Jin-Si R. Over, Christopher R. Sherwood
Publikováno v:
Shore & Beach. :31-40
Hurricanes are known to play a critical role in reshaping coastlines, particularly on the open ocean coast in cases of overwash, but storm induced seaward-directed flow and responses are often ignored or un-documented. Subaerial evidence for seaward
Autor:
Shah Nafis Rafique, Ashley Ellenson, Luke A. Taylor, Somya D. Mohanty, Eli D. Lazarus, I. R. B. Reeves, R. Palermo, K. Anarde, Jessamin A. Straub, Jin-Si R. Over, N. Cohn, Hannah Williams, Megan Gillen, Andrew D. Ashton, Katherine A. Castagno, K. M. Ratliff, Paige A. Hovenga, E. J. Wallace, Jonathan A. Warrick, Phillipe A. Wernette, Tomas Beuzen, Matthew P. Conlin, Daniel Buscombe, Lily H. Sanborn, Evan B. Goldstein
Publikováno v:
Earth and Space Science, Vol 8, Iss 9, Pp n/a-n/a (2021)
Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for example, associated with each image. Data‐driven models are only as good as the data used for training, and this points to
Autor:
Josephine Chiarello, Jin-Si R. Over, Yi Song, Emily Hauf, Jenelle Wallace, Thomas J. Algeo, Geoffrey J. Gilleaudeau, D. Jeffrey Over
Publikováno v:
Palaeogeography, Palaeoclimatology, Palaeoecology. 524:137-149
Recognition of stratigraphic hiatuses in fine-grained siliciclastic sedimentary rocks can be challenging but is feasible using high-resolution biostratigraphic and chemostratigraphic data within a regional correlation framework. In this case study of