Labeled images of emerged salmonids in a riverine environment

These data enable the development of machine learned models to detect unintended passage of salmonids over in-stream barriers. Such models are key to fully characterizing the effectiveness of selective passage systems, as they detect and quantify fish passage which occurs outside of the intended tra...

Full description

Saved in:
Bibliographic Details
Published inBMC research notes Vol. 17; no. 1; pp. 348 - 5
Main Authors Jagadeesan, Sethu Mettukulam, Gregory, Jonathan, Leh, Jordan, Eickholt, Jesse, Zielinski, Daniel P
Format Journal Article
LanguageEnglish
Published England BioMed Central Ltd 27.11.2024
BioMed Central
BMC
Subjects
Online AccessGet full text

Cover

Loading…
Abstract These data enable the development of machine learned models to detect unintended passage of salmonids over in-stream barriers. Such models are key to fully characterizing the effectiveness of selective passage systems, as they detect and quantify fish passage which occurs outside of the intended transit passage and selection mechanism. These data were used to construct custom surveillance tools for FishPass ( https://www.glfc.org/fishpass.php ), a 20-year restoration project to provide selective up- and down-stream passage of desirable fishes while simultaneously blocking or removing undesirable fishes. The datasets contain over 2300 annotated images of emerged salmonids collected in a natural riverine environment. The images stem from surveillance video collected during 2022 and 2023 fall runs of several pacific salmonid species introduced to the Laurentian Great Lakes on the Boardman (Ottaway) River in Traverse City, MI, USA. In addition to images of fully emerged salmonids, datasets are provided containing images of partially emerged salmonids, fully submerged fish, and other wildlife present in a riverine environment. The environmental conditions represented by most of the images were clear or partly cloudy. These datasets could be used to develop custom object detection models for emerged fish in riverine environments.
AbstractList These data enable the development of machine learned models to detect unintended passage of salmonids over in-stream barriers. Such models are key to fully characterizing the effectiveness of selective passage systems, as they detect and quantify fish passage which occurs outside of the intended transit passage and selection mechanism. These data were used to construct custom surveillance tools for FishPass (https://www.glfc.org/fishpass.php), a 20-year restoration project to provide selective up- and down-stream passage of desirable fishes while simultaneously blocking or removing undesirable fishes.
Objectives These data enable the development of machine learned models to detect unintended passage of salmonids over in-stream barriers. Such models are key to fully characterizing the effectiveness of selective passage systems, as they detect and quantify fish passage which occurs outside of the intended transit passage and selection mechanism. These data were used to construct custom surveillance tools for FishPass ( Data description The datasets contain over 2300 annotated images of emerged salmonids collected in a natural riverine environment. The images stem from surveillance video collected during 2022 and 2023 fall runs of several pacific salmonid species introduced to the Laurentian Great Lakes on the Boardman (Ottaway) River in Traverse City, MI, USA. In addition to images of fully emerged salmonids, datasets are provided containing images of partially emerged salmonids, fully submerged fish, and other wildlife present in a riverine environment. The environmental conditions represented by most of the images were clear or partly cloudy. These datasets could be used to develop custom object detection models for emerged fish in riverine environments. Keywords: Annotated images of fish, Object detection, Labelled fish dataset, Emerged fish, Unintended passage
These data enable the development of machine learned models to detect unintended passage of salmonids over in-stream barriers. Such models are key to fully characterizing the effectiveness of selective passage systems, as they detect and quantify fish passage which occurs outside of the intended transit passage and selection mechanism. These data were used to construct custom surveillance tools for FishPass ( https://www.glfc.org/fishpass.php ), a 20-year restoration project to provide selective up- and down-stream passage of desirable fishes while simultaneously blocking or removing undesirable fishes.OBJECTIVESThese data enable the development of machine learned models to detect unintended passage of salmonids over in-stream barriers. Such models are key to fully characterizing the effectiveness of selective passage systems, as they detect and quantify fish passage which occurs outside of the intended transit passage and selection mechanism. These data were used to construct custom surveillance tools for FishPass ( https://www.glfc.org/fishpass.php ), a 20-year restoration project to provide selective up- and down-stream passage of desirable fishes while simultaneously blocking or removing undesirable fishes.The datasets contain over 2300 annotated images of emerged salmonids collected in a natural riverine environment. The images stem from surveillance video collected during 2022 and 2023 fall runs of several pacific salmonid species introduced to the Laurentian Great Lakes on the Boardman (Ottaway) River in Traverse City, MI, USA. In addition to images of fully emerged salmonids, datasets are provided containing images of partially emerged salmonids, fully submerged fish, and other wildlife present in a riverine environment. The environmental conditions represented by most of the images were clear or partly cloudy. These datasets could be used to develop custom object detection models for emerged fish in riverine environments.DATA DESCRIPTIONThe datasets contain over 2300 annotated images of emerged salmonids collected in a natural riverine environment. The images stem from surveillance video collected during 2022 and 2023 fall runs of several pacific salmonid species introduced to the Laurentian Great Lakes on the Boardman (Ottaway) River in Traverse City, MI, USA. In addition to images of fully emerged salmonids, datasets are provided containing images of partially emerged salmonids, fully submerged fish, and other wildlife present in a riverine environment. The environmental conditions represented by most of the images were clear or partly cloudy. These datasets could be used to develop custom object detection models for emerged fish in riverine environments.
These data enable the development of machine learned models to detect unintended passage of salmonids over in-stream barriers. Such models are key to fully characterizing the effectiveness of selective passage systems, as they detect and quantify fish passage which occurs outside of the intended transit passage and selection mechanism. These data were used to construct custom surveillance tools for FishPass ( https://www.glfc.org/fishpass.php ), a 20-year restoration project to provide selective up- and down-stream passage of desirable fishes while simultaneously blocking or removing undesirable fishes. The datasets contain over 2300 annotated images of emerged salmonids collected in a natural riverine environment. The images stem from surveillance video collected during 2022 and 2023 fall runs of several pacific salmonid species introduced to the Laurentian Great Lakes on the Boardman (Ottaway) River in Traverse City, MI, USA. In addition to images of fully emerged salmonids, datasets are provided containing images of partially emerged salmonids, fully submerged fish, and other wildlife present in a riverine environment. The environmental conditions represented by most of the images were clear or partly cloudy. These datasets could be used to develop custom object detection models for emerged fish in riverine environments.
Abstract Objectives These data enable the development of machine learned models to detect unintended passage of salmonids over in-stream barriers. Such models are key to fully characterizing the effectiveness of selective passage systems, as they detect and quantify fish passage which occurs outside of the intended transit passage and selection mechanism. These data were used to construct custom surveillance tools for FishPass ( https://www.glfc.org/fishpass.php ), a 20-year restoration project to provide selective up- and down-stream passage of desirable fishes while simultaneously blocking or removing undesirable fishes. Data description The datasets contain over 2300 annotated images of emerged salmonids collected in a natural riverine environment. The images stem from surveillance video collected during 2022 and 2023 fall runs of several pacific salmonid species introduced to the Laurentian Great Lakes on the Boardman (Ottaway) River in Traverse City, MI, USA. In addition to images of fully emerged salmonids, datasets are provided containing images of partially emerged salmonids, fully submerged fish, and other wildlife present in a riverine environment. The environmental conditions represented by most of the images were clear or partly cloudy. These datasets could be used to develop custom object detection models for emerged fish in riverine environments.
Audience Academic
Author Leh, Jordan
Gregory, Jonathan
Eickholt, Jesse
Jagadeesan, Sethu Mettukulam
Zielinski, Daniel P
Author_xml – sequence: 1
  givenname: Sethu Mettukulam
  surname: Jagadeesan
  fullname: Jagadeesan, Sethu Mettukulam
  organization: Department of Computer Science, Central Michigan University, Mount Pleasant, MI, 48859, USA
– sequence: 2
  givenname: Jonathan
  surname: Gregory
  fullname: Gregory, Jonathan
  organization: Department of Computer Science, Central Michigan University, Mount Pleasant, MI, 48859, USA
– sequence: 3
  givenname: Jordan
  surname: Leh
  fullname: Leh, Jordan
  organization: Department of Statistics, Actuarial, and Data Sciences, Central Michigan University, Mount Pleasant, MI, 48859, USA
– sequence: 4
  givenname: Jesse
  surname: Eickholt
  fullname: Eickholt, Jesse
  email: eickh1jl@cmich.edu
  organization: Department of Computer Science, Central Michigan University, Mount Pleasant, MI, 48859, USA. eickh1jl@cmich.edu
– sequence: 5
  givenname: Daniel P
  surname: Zielinski
  fullname: Zielinski, Daniel P
  organization: Great Lakes Fishery Commission, Ann Arbor, MI, USA
BackLink https://www.ncbi.nlm.nih.gov/pubmed/39605019$$D View this record in MEDLINE/PubMed
BookMark eNptkkuLFDEUhYOMOA_9Ay6kwI0uasyjKo-FyDD4aGiYjboNtyo3ZYaqZEyqG_33RnuUaZAQcjn57uEeknNyElNEQp4zesmYlm8KE4x2LeV1K8p4yx-RM6Z62dKe0pMH9Sk5L-WWUsm0Zk_IqTCyysyckbdbGHBG14QFJixN8g0umKeqFJiXFIMrTYgNNDnsMYeIDcZ9yCkuGNen5LGHueCz-_OCfPnw_vP1p3Z783FzfbVtnZDd2jrdUTFSNXCDxnAEaYaxQ_BejQqFU8yh4rqn3oAaO0e5MZozFNx7cN6JC7I5-LoEt_Yu12HzT5sg2D9CypOFvIZxRsudMUPfdcIY7ECzGk8JKTnAoJzzUL3eHbzudsOCbqwxMsxHpsc3MXyzU9pbxiQV2ojq8OreIafvOyyrXUIZcZ4hYtoVK5gQSqhe8Yq-PKAT1NlC9Klajr9xe6WZppJT2Vfq8j9UXQ6XMNZH96HqRw2vjxoqs-KPdYJdKXZz8_WYffEw77-gf_-A-AXf6rQP
ContentType Journal Article
Copyright 2024. The Author(s).
COPYRIGHT 2024 BioMed Central Ltd.
The Author(s) 2024 2024
Copyright_xml – notice: 2024. The Author(s).
– notice: COPYRIGHT 2024 BioMed Central Ltd.
– notice: The Author(s) 2024 2024
DBID CGR
CUY
CVF
ECM
EIF
NPM
IOV
7X8
5PM
DOA
DOI 10.1186/s13104-024-07012-2
DatabaseName Medline
MEDLINE
MEDLINE (Ovid)
MEDLINE
MEDLINE
PubMed
Opposing Viewpoints Resource Center
MEDLINE - Academic
PubMed Central (Full Participant titles)
Directory of Open Access Journals
DatabaseTitle MEDLINE
Medline Complete
MEDLINE with Full Text
PubMed
MEDLINE (Ovid)
MEDLINE - Academic
DatabaseTitleList


MEDLINE - Academic
MEDLINE

Database_xml – sequence: 1
  dbid: DOA
  name: Directory of Open Access Journals
  url: https://www.doaj.org/
  sourceTypes: Open Website
– sequence: 2
  dbid: NPM
  name: PubMed
  url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
– sequence: 3
  dbid: EIF
  name: MEDLINE
  url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search
  sourceTypes: Index Database
DeliveryMethod fulltext_linktorsrc
Discipline Biology
EISSN 1756-0500
EndPage 5
ExternalDocumentID oai_doaj_org_article_2d99b544399e4a81abe73662aab7ddfa
A818062065
39605019
Genre Journal Article
GeographicLocations United States
Michigan
GeographicLocations_xml – name: United States
– name: Michigan
GrantInformation_xml – fundername: Great Lakes Fishery Commission
  sequence: 0
  grantid: 2022 EIC 793012
GroupedDBID ---
-A0
0R~
23N
2WC
3V.
53G
5GY
5VS
6J9
7X7
88E
8FE
8FH
8FI
8FJ
AAFWJ
AAJSJ
ABDBF
ABUWG
ACGFO
ACGFS
ACIHN
ACMJI
ACPRK
ACRMQ
ADBBV
ADINQ
ADRAZ
ADUKV
AEAQA
AFKRA
AFPKN
AHBYD
AHMBA
AHYZX
ALIPV
ALMA_UNASSIGNED_HOLDINGS
AMKLP
AMTXH
AOIJS
BAPOH
BAWUL
BBNVY
BCNDV
BENPR
BFQNJ
BHPHI
BMC
BPHCQ
BVXVI
C24
C6C
CCPQU
CGR
CS3
CUY
CVF
DIK
E3Z
EBD
EBLON
EBS
ECM
EIF
EMOBN
ESX
F5P
FYUFA
GROUPED_DOAJ
GX1
HCIFZ
HMCUK
HYE
IAO
IEA
IHR
INH
INR
IOV
ITC
KQ8
LGEZI
LK8
LOTEE
M1P
M7P
MK0
M~E
NADUK
NPM
NXXTH
O5R
O5S
OK1
P2P
PGMZT
PIMPY
PQQKQ
PROAC
PSQYO
RBZ
RNS
ROL
RPM
RSV
SBL
SOJ
SV3
TR2
TUS
UKHRP
~8M
7X8
5PM
ID FETCH-LOGICAL-d364t-d8403c07b29e992ea69bc4eaff7c7e3d71de72850f9a7c4d0299821e32ffadfd3
IEDL.DBID RPM
ISSN 1756-0500
IngestDate Thu Dec 05 08:01:40 EST 2024
Fri Nov 29 05:16:52 EST 2024
Wed Dec 04 16:59:58 EST 2024
Thu Dec 05 15:00:17 EST 2024
Tue Dec 10 03:41:34 EST 2024
Thu Dec 05 13:28:03 EST 2024
Thu Dec 05 03:38:39 EST 2024
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 1
Keywords Unintended passage
Labelled fish dataset
Emerged fish
Annotated images of fish
Object detection
Language English
License 2024. The Author(s).
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-d364t-d8403c07b29e992ea69bc4eaff7c7e3d71de72850f9a7c4d0299821e32ffadfd3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
OpenAccessLink https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11603893/
PMID 39605019
PQID 3133737572
PQPubID 23479
PageCount 5
ParticipantIDs doaj_primary_oai_doaj_org_article_2d99b544399e4a81abe73662aab7ddfa
pubmedcentral_primary_oai_pubmedcentral_nih_gov_11603893
proquest_miscellaneous_3133737572
gale_infotracmisc_A818062065
gale_infotracacademiconefile_A818062065
gale_incontextgauss_IOV_A818062065
pubmed_primary_39605019
PublicationCentury 2000
PublicationDate 2024-11-27
PublicationDateYYYYMMDD 2024-11-27
PublicationDate_xml – month: 11
  year: 2024
  text: 2024-11-27
  day: 27
PublicationDecade 2020
PublicationPlace England
PublicationPlace_xml – name: England
– name: London
PublicationTitle BMC research notes
PublicationTitleAlternate BMC Res Notes
PublicationYear 2024
Publisher BioMed Central Ltd
BioMed Central
BMC
Publisher_xml – sequence: 0
  name: BioMed Central Ltd
– sequence: 0
  name: BMC
– name: BioMed Central
SSID ssj0061881
Score 2.4211051
Snippet These data enable the development of machine learned models to detect unintended passage of salmonids over in-stream barriers. Such models are key to fully...
Objectives These data enable the development of machine learned models to detect unintended passage of salmonids over in-stream barriers. Such models are key...
Abstract Objectives These data enable the development of machine learned models to detect unintended passage of salmonids over in-stream barriers. Such models...
SourceID doaj
pubmedcentral
proquest
gale
pubmed
SourceType Open Website
Open Access Repository
Aggregation Database
Index Database
StartPage 348
SubjectTerms Analysis
Animals
Annotated images of fish
Data Note
Diseases
Emerged fish
Fishes
Genetic aspects
Growth
Labelled fish dataset
Machine learning
Michigan
Natural resources
Object detection
Protection and preservation
Rivers
Salmonidae
Unintended passage
United States
SummonAdditionalLinks – databaseName: Directory of Open Access Journals
  dbid: DOA
  link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LixQxEA6yIHgR37auEkXwFDaP7jwOHlZxWUX04sreQh4VHdjtke2Zw_77rXT3yDQevHjtCk1SVUl9BVVfEfIGOmMK6MR4UR1rI965kLvIoI1Wa65siCPb51d9etZ-Pu_O90Z91ZqwiR54UtyRzM7FStLmHLTBihDBKK1lCNHkXCZoxOUumZreYC2sFbsWGauPBoEopmUYjxi6uJBMzhT9fz_Ee5FoWSW5F3ZO7pG7M16kx9M-75Nb0D8gt6cJktcPybsvuNMLyHR1iQ_DQNeFwthQmekQLtDFVnmgq54GelULMBBS0r3Wtkfk7OTj9w-nbJ6IwLLS7YZlTMdU4iZKB85JCNrF1EIoxSQDKhuRwUjb8eKCSW3mGGysFKBkKSGXrB6Tg37dw1NCXcdFwtiO_0PtZu2g45mDcsKljLCmIe-rgvzvifTCVxrq8QMax8_G8f8yTkNeV_X6SjTR10qWn2E7DP7Ttx_-uDaZa4kIqCFv50VljYpOYW4MwH1WbqrFysPFSrwJaSF-tbOir6JaPtbDejt4hZm4UaYzsiFPJqv-OZjCHA514RpiF_ZenHwp6Ve_RiJuUWd0I-B79j909ZzckdVBhWDSHJKDzdUWXiDg2cSXo2_fALJ1_pM
  priority: 102
  providerName: Directory of Open Access Journals
Title Labeled images of emerged salmonids in a riverine environment
URI https://www.ncbi.nlm.nih.gov/pubmed/39605019
https://www.proquest.com/docview/3133737572
https://pubmed.ncbi.nlm.nih.gov/PMC11603893
https://doaj.org/article/2d99b544399e4a81abe73662aab7ddfa
Volume 17
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwELbaIiQuqLxDyyogJE7uxo_Y8YHDtmpVVlAqoGhvkZ8lUjepNrsH_j3jbII24sYpUuxE8cxnf-No5jNC730uZfDC4iywHHMDc0673GDPTSFExgptOrXPK3F5w-eLfLGHxFAL0yXtW1Od1HfLk7r61eVW3i_tdMgTm15_OSPxbGQg2uk-2gf-Hfbo2_VXkKIgQ3lMIaYtgQiGY-AiDPAmFMcDbBgE7nknr9Mp9f-7Hu8Q0jhZcod9Lg7R4z5sTGfbz3uC9nz9FD3cHiT5-xn6-Fkb4A-XVktYH9q0Canv6ipd2uo7QFrl2rSqU52uYh4GRJbpToXbc3Rzcf7j7BL3ByNgxwRfYwe7MmYzaajySlGvhTKWex2CtNIzJ4nzkhZ5FpSWlrsMOKegxDMagnbBsRfooG5q_wqlCqxggeLhfTnnTiifZy7zTBFlHUQ3CTqNBirvt9oXZVSj7m40q9uy90lJnVImCukp5bkuCAxZMiGo1kY6F3SC3kXzllFvoo4JLbd607blp68_y1msNRcUAqEEfeg7hQYMbXVfHwDfGSWqRj2PRz1hQthR89vBi2VsillktW82bclgQy6ZzCVN0MutV_8ObEBEgoqRv0cjH7cAPDs97gGOr___0SP0iEaEEoKpPEYH69XGv4FoZ20mAPGFnKAHs9n8-xyup-dX198m3b-DSQf8P4YhA50
link.rule.ids 230,314,727,780,784,864,885,2102,27924,27925,31720,33745,53791,53793
linkProvider National Library of Medicine
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwELZKEYJLxbMECgSExMnd2E7s-MChVFRb2BYOLerN8rNE6ibVZvfQf884D7QRN66xE8Uzn_2NpZlvEProCyGC5xZngRU4N7DntCsM9rkpOc9YqU2n9nnO55f5t6viagfxsRamS9q3pjqsb5aHdfW7y628XdrZmCc2-3l2TGJvZCDa2T10v2BCkvGW3p_AnJQlGQtkSj5rCcQwOQY2wgBwQnFsYcMgdC86gZ1Oq__fE3mLkqbpklv8c_IY7Q2BY3rU_-ATtOPrp-hB30ry7hn6vNAGGMSl1RJOiDZtQuq7ykqXtvoGsFa5Nq3qVKermIkBsWW6VeP2HF2efL04nuOhNQJ2jOdr7OBexmwmDJVeSuo1l8bmXocgrPDMCeK8oGWRBamFzV0GrFNS4hkNQbvg2Au0Wze1f4lSCVawQPLwvSLPHZe-yFzmmSTSOohvEvQlGkjd9uoXKupRdw-a1bUavKKok9JEKT0pfa5LAksWjHOqtRHOBZ2gD9G8KipO1DGl5Vpv2lad_viljmK1OacQCiXo0zApNGBoq4cKAfjPKFI1mXkwmQlbwk6G349eVHEo5pHVvtm0isGVXDBRCJqg_d6rfxc2IiJB5cTfk5VPRwCgnSL3CMhX___qO_RwfnG2UIvT8--v0SMa0UoIpuIA7a5XG_8GYp-1edsB_Q_zogHw
linkToPdf http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwELagCMQF8SZQICAkTm5iO7bjA4dSWLVQSg8U9Rb5WSJ1k9Vm98C_Z5wH2ogb19iJ4pnPnrH0zTcIvfNcyuCFxXlgHBcG9px23GBfmFKInJXa9GqfZ-L4ovhyyS9HVmU30ioba-qD5np50NS_em7lammziSeWnX87IrE3MgTabOVCdhPd4gxQNt3Uh1NYkLIkU5FMKbKOQB5TYIhIGEBOKI5tbBik77wX2en1-v89lXfC0pwyuRODFvfRvTF5TA-Hn3yAbvjmIbo9tJP8_Qh9ONUGoohL6yWcEl3ahtT31ZUu7fQ14K12XVo3qU7XkY0B-WW6U-f2GF0sPv84OsZjewTsmCg22MHdjNlcGqq8UtRroYwtvA5BWumZk8R5SUueB6WlLVwOkaekxDMagnbBsSdor2kb_wylCqxgIdDD93hROKE8z13umSLKOshxEvQxGqhaDQoYVdSk7h-066tq9ExFnVImyukp5QtdEliyZEJQrY10LugEvY3mraLqRBNpLVd623XVyfef1WGsOBcU0qEEvR8nhRYMbfVYJQD_GYWqZjP3ZzNhW9jZ8JvJi1UcilyyxrfbrmJwLZdMckkT9HTw6t-FTYhIUDnz92zl8xEAaa_KPYHy-f-_-hrdOf-0qE5Pzr6-QHdpBCshmMp9tLdZb_1LSH825lWP8z-sHwMD
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Labeled+images+of+emerged+salmonids+in+a+riverine+environment&rft.jtitle=BMC+research+notes&rft.au=Jagadeesan%2C+Sethu+Mettukulam&rft.au=Gregory%2C+Jonathan&rft.au=Leh%2C+Jordan&rft.au=Eickholt%2C+Jesse&rft.date=2024-11-27&rft.pub=BioMed+Central+Ltd&rft.issn=1756-0500&rft.eissn=1756-0500&rft.volume=17&rft.issue=1&rft_id=info:doi/10.1186%2Fs13104-024-07012-2&rft.externalDocID=A818062065
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1756-0500&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1756-0500&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1756-0500&client=summon