Author Correction:Deep neural networks for automated detection of marine mammal species (Scientific Reports, (2020), 10, 1, (607), 10.1038/s41598-020-57549-y)

The original version of this Article contained errors. Table 1 omitted to reference the experimental data and its funding sources. As the result, References 78-83 were omitted from Table 1. Added References are listed below: Hatch, Leila T., et al. Quantifying loss of acoustic communication space fo...

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Published in:Scientific Reports
Main Authors: Shiu, Yu, Palmer, K. J., Roch, Marie A., Fleishman, Erica, Liu, Xiaobai, Nosal, Eva Marie, Helble, Tyler, Cholewiak, Danielle, Gillespie, Douglas, Klinck, Holger
Format: Article in Journal/Newspaper
Language:English
Published: 2021
Subjects:
Online Access:https://risweb.st-andrews.ac.uk/portal/en/researchoutput/author-correction(a7d70131-3ed1-4032-8921-18f2da4d83da).html
https://doi.org/10.1038/s41598-021-00460-x
http://www.scopus.com/inward/record.url?scp=85117700119&partnerID=8YFLogxK
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spelling ftunstandrewcris:oai:risweb.st-andrews.ac.uk:publications/a7d70131-3ed1-4032-8921-18f2da4d83da 2023-05-15T15:37:00+02:00 Author Correction:Deep neural networks for automated detection of marine mammal species (Scientific Reports, (2020), 10, 1, (607), 10.1038/s41598-020-57549-y) Shiu, Yu Palmer, K. J. Roch, Marie A. Fleishman, Erica Liu, Xiaobai Nosal, Eva Marie Helble, Tyler Cholewiak, Danielle Gillespie, Douglas Klinck, Holger 2021-12 https://risweb.st-andrews.ac.uk/portal/en/researchoutput/author-correction(a7d70131-3ed1-4032-8921-18f2da4d83da).html https://doi.org/10.1038/s41598-021-00460-x http://www.scopus.com/inward/record.url?scp=85117700119&partnerID=8YFLogxK eng eng info:eu-repo/semantics/openAccess Shiu , Y , Palmer , K J , Roch , M A , Fleishman , E , Liu , X , Nosal , E M , Helble , T , Cholewiak , D , Gillespie , D & Klinck , H 2021 , ' Author Correction : Deep neural networks for automated detection of marine mammal species (Scientific Reports, (2020), 10, 1, (607), 10.1038/s41598-020-57549-y) ' , Scientific Reports , vol. 11 , no. 1 , 21189 . https://doi.org/10.1038/s41598-021-00460-x article 2021 ftunstandrewcris https://doi.org/10.1038/s41598-021-00460-x https://doi.org/10.1038/s41598-020-57549-y 2022-07-21T07:01:41Z The original version of this Article contained errors. Table 1 omitted to reference the experimental data and its funding sources. As the result, References 78-83 were omitted from Table 1. Added References are listed below: Hatch, Leila T., et al. Quantifying loss of acoustic communication space for right whales in and around a US National Marine Sanctuary. Conservation Biology 26.6, 983-994 (2012). Clark, C.W., et al. An ocean observing system for large-scale monitoring and mapping of noise throughout the Stellwagen Bank National Marine Sanctuary. Cornell University, Ithaca, NY (2010). Cholewiak, D., et al. Communicating amidst the noise: modeling the aggregate influence of ambient and vessel noise on baleen whale communication space in a national marine sanctuary. Endangered Species Research, 36, 59-75. (2018). Rice, A. N. et al. Baseline bioacoustic characterization for offshore alternative energy development in North Carolina and Georgia wind planning areas. U.S. Department of the Interior, Bureau of Ocean Energy Management, Gulf of Mexico OCS Region., New Orleans, LA. (2015). Salisbury, D. P., Estabrook, B. J., Klinck, H., & Rice., A. N. Understanding marine mammal presence in the Virginia offshore wind energy area. US Department of the Interior, Bureau of Ocean Energy Management, Sterling, VA. (2019) Bailey, H. et al. Determining offshore use by marine mammals and ambient noise levels using passive acoustic monitoring. U.S. Department of the Interior, Bureau of Ocean Energy Management., Sterling, VA. (2018) Consequently, the legend of Table 1 has been corrected accordingly, “Number of upcalls indicates the number of upcalls annotated by trained analysts. For deployments with two or more recorders, the number of upcalls indicates the total number of upcalls detected across all recorders. Shaded rows indicate data used to train neural networks. Non-shaded rows represent evaluation data. Negative examples for the Kaggle data represent the false detections flagged by the analysts as derived from non-right ... Article in Journal/Newspaper baleen whale University of St Andrews: Research Portal Orleans ENVELOPE(-60.667,-60.667,-63.950,-63.950) Salisbury ENVELOPE(-153.617,-153.617,-85.633,-85.633) Scientific Reports 11 1
institution Open Polar
collection University of St Andrews: Research Portal
op_collection_id ftunstandrewcris
language English
description The original version of this Article contained errors. Table 1 omitted to reference the experimental data and its funding sources. As the result, References 78-83 were omitted from Table 1. Added References are listed below: Hatch, Leila T., et al. Quantifying loss of acoustic communication space for right whales in and around a US National Marine Sanctuary. Conservation Biology 26.6, 983-994 (2012). Clark, C.W., et al. An ocean observing system for large-scale monitoring and mapping of noise throughout the Stellwagen Bank National Marine Sanctuary. Cornell University, Ithaca, NY (2010). Cholewiak, D., et al. Communicating amidst the noise: modeling the aggregate influence of ambient and vessel noise on baleen whale communication space in a national marine sanctuary. Endangered Species Research, 36, 59-75. (2018). Rice, A. N. et al. Baseline bioacoustic characterization for offshore alternative energy development in North Carolina and Georgia wind planning areas. U.S. Department of the Interior, Bureau of Ocean Energy Management, Gulf of Mexico OCS Region., New Orleans, LA. (2015). Salisbury, D. P., Estabrook, B. J., Klinck, H., & Rice., A. N. Understanding marine mammal presence in the Virginia offshore wind energy area. US Department of the Interior, Bureau of Ocean Energy Management, Sterling, VA. (2019) Bailey, H. et al. Determining offshore use by marine mammals and ambient noise levels using passive acoustic monitoring. U.S. Department of the Interior, Bureau of Ocean Energy Management., Sterling, VA. (2018) Consequently, the legend of Table 1 has been corrected accordingly, “Number of upcalls indicates the number of upcalls annotated by trained analysts. For deployments with two or more recorders, the number of upcalls indicates the total number of upcalls detected across all recorders. Shaded rows indicate data used to train neural networks. Non-shaded rows represent evaluation data. Negative examples for the Kaggle data represent the false detections flagged by the analysts as derived from non-right ...
format Article in Journal/Newspaper
author Shiu, Yu
Palmer, K. J.
Roch, Marie A.
Fleishman, Erica
Liu, Xiaobai
Nosal, Eva Marie
Helble, Tyler
Cholewiak, Danielle
Gillespie, Douglas
Klinck, Holger
spellingShingle Shiu, Yu
Palmer, K. J.
Roch, Marie A.
Fleishman, Erica
Liu, Xiaobai
Nosal, Eva Marie
Helble, Tyler
Cholewiak, Danielle
Gillespie, Douglas
Klinck, Holger
Author Correction:Deep neural networks for automated detection of marine mammal species (Scientific Reports, (2020), 10, 1, (607), 10.1038/s41598-020-57549-y)
author_facet Shiu, Yu
Palmer, K. J.
Roch, Marie A.
Fleishman, Erica
Liu, Xiaobai
Nosal, Eva Marie
Helble, Tyler
Cholewiak, Danielle
Gillespie, Douglas
Klinck, Holger
author_sort Shiu, Yu
title Author Correction:Deep neural networks for automated detection of marine mammal species (Scientific Reports, (2020), 10, 1, (607), 10.1038/s41598-020-57549-y)
title_short Author Correction:Deep neural networks for automated detection of marine mammal species (Scientific Reports, (2020), 10, 1, (607), 10.1038/s41598-020-57549-y)
title_full Author Correction:Deep neural networks for automated detection of marine mammal species (Scientific Reports, (2020), 10, 1, (607), 10.1038/s41598-020-57549-y)
title_fullStr Author Correction:Deep neural networks for automated detection of marine mammal species (Scientific Reports, (2020), 10, 1, (607), 10.1038/s41598-020-57549-y)
title_full_unstemmed Author Correction:Deep neural networks for automated detection of marine mammal species (Scientific Reports, (2020), 10, 1, (607), 10.1038/s41598-020-57549-y)
title_sort author correction:deep neural networks for automated detection of marine mammal species (scientific reports, (2020), 10, 1, (607), 10.1038/s41598-020-57549-y)
publishDate 2021
url https://risweb.st-andrews.ac.uk/portal/en/researchoutput/author-correction(a7d70131-3ed1-4032-8921-18f2da4d83da).html
https://doi.org/10.1038/s41598-021-00460-x
http://www.scopus.com/inward/record.url?scp=85117700119&partnerID=8YFLogxK
long_lat ENVELOPE(-60.667,-60.667,-63.950,-63.950)
ENVELOPE(-153.617,-153.617,-85.633,-85.633)
geographic Orleans
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geographic_facet Orleans
Salisbury
genre baleen whale
genre_facet baleen whale
op_source Shiu , Y , Palmer , K J , Roch , M A , Fleishman , E , Liu , X , Nosal , E M , Helble , T , Cholewiak , D , Gillespie , D & Klinck , H 2021 , ' Author Correction : Deep neural networks for automated detection of marine mammal species (Scientific Reports, (2020), 10, 1, (607), 10.1038/s41598-020-57549-y) ' , Scientific Reports , vol. 11 , no. 1 , 21189 . https://doi.org/10.1038/s41598-021-00460-x
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