[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118600-en":3,"doc-seo-118600-105":30,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118600,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",6,"Technology","Auto machine learning tools to distinguish between two killer whale ecotypes","Auto machine learning methods are developed to distinguish two killer whale ecotypes—residents (R-type) that focus on fish and transients, including Bigg’s killer whales (T-type) that focus on mammals. The study addresses the lack of automated, non-time-intensive photo identification, where manual digitizing of fin contours has limited routine assessment. By leveraging machine-learning and image-processing approaches, the work aims to improve classification accuracy and support more reliable, ecotype-specific monitoring and evaluation.","University of Southern Denmark  \nAuto machine learning tools to distinguish between two killer whale ecotypes  \nIsmail, Mohamed; Fedutin, Ivan; Hoyt, Erich; Ivkovich, Tatiana; Filatova, Olga  \nPublished in:  \nMarine Mammal Science  \nDOI:  \n10.1111/mms.13175  \nPublication date: 2025  \nDocument version:  \nFinal published version  \nDocument license: CC BY-NC-ND  \nCitation for pulished version (APA):  \nIsmail, M. , Fedutin, I. , Hoyt, E. , Ivkovich, T. , & Filatova, O. (2025) . Auto machine learning tools to distinguish between two killer whale ecotypes. Marine Mammal Science, 41(1), Article e13175 .  \n[https://doi.org/10.1111/mms.13175](https://doi.org/10.1111/mms.13175)  \nGo to publication entry in University of Southern Denmark's Research Portal  \nTerms of use  \nThis work is brought to you by the University of Southern Denmark.  \nUnless otherwise specified it has been shared according to the terms for self-archiving.  \nIf no other license is stated, these terms apply:  \n• You may download this work for personal use only.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying this open access version  \nIf you believe that this document breaches copyright please contact us providing details and we will investigate your claim. Please direct all enquiries to [puresupport@bib.sdu.dk](puresupport@bib.sdu.dk)  \nDownload date: 02. Aug. 2026  \nReceived: 9  \nDecember 2023  \nAccepted: 26 July 2024  \nDOI: 10.1111/mms.13175  \nNOTE  \nAuto machine learning tools to distinguish between two killer whale ecotypes  \nMohamed E. Ismail 1,2  | Ivan D. Fedutin 3,4  | Erich Hoyt 5  | Tatiana V. Ivkovich 6  | Olga A. Filatova 3,7   \n1Department of Vertebrate Zoology, Faculty of Biology, Lomonosov Moscow State University, Moscow, Russia 2Department of Marine Science, Faculty of Science, Port-Said University, Port Said, Egypt  \n3Department of Biology, University of Southern Denmark, Odense, Denmark 4Fjord and Baelt, Kerteminde, Denmark  \n5Whale and Dolphin Conservation, Bridport, Dorset, UK 6School “Kit”, Saint Petersburg, Russia  \n7SDU Climate Cluster, University of Southern Denmark, Odense, Denmark  \nCorrespondence  \nOlga A. Filatova, Department of Biology, University of Southern Denmark, Campusvej 55, 5230 Odense, Denmark.  \nEmail: fi[latova@biology.sdu.dk](latova@biology.sdu.dk)  \nFunding information  \nRussian Foundation for Basic Research; Rufford Foundation; Whale and Dolphin Conservation; Russian Science Foundation  \nThe killer whale, despite being considered a single species, exhibits various ecotypes (genetically and ecologically distinct populations), that focus on a specific type of prey (Ford et al., 1998, 2000; Pitman et al., 2011; Pitman & Ensor, 2003; Saulitis et al., 2000) . In the northwestern Pacific, killer whales comprise two ecotypes: residents or R-type (fish-eaters) and transients, also called Bigg's killer whales, or T-type (mammal-eaters) (Filatova et al., 2018, 2019; Ismail et al., 2023) . These ecotypes are frequently found in the same areas, but they do not engage in social activities and are reproductively isolated (Filatova, Borisova, et al., 2015; Foote et al., 2011; Morin et al., 2010) . This isolation is linked to significant variations in their morphology (Baird & Stacey, 1988; Kotik et al., 2023), ecology (Bigg, 1987), behavior (Morton, 1990), acoustic communication (Deecke et al., 2005; Filatova, Fedutin, et al., 2015; Foote & Nystuen, 2008), social structure (Baird & Dill, 1996), diet (Borisova et al., 2020; Filatova et al., 2023; Herman et al., 2005), and other aspects. The genetic distinction between the ecotypes has been described both for eastern and western North Pacific (Filatova, Borisova, et al., 2015; Hoelzel et al., 2007; Morin et al., 2010; Parsons et al., 2013), but the morphological variation was studied mostly in the eastern North Pacific (Baird & Stacey, 1988; Emmons et al., 2019; Kotik et al., 2023; P","cbCaipAFDcGnOEC8","https://ap.wps.com/l/cbCaipAFDcGnOEC8","pdf",1462605,1,12,"English","en",105,"# Background and motivation\n## Ecotypes and their biological differences\n## Limitations of current identification approaches\n# Machine learning approach\n## Image-based methods and CNN\n# Evaluation and expected impact","[{\"question\":\"How does the study use machine learning for ecotype identification?\",\"answer\":\"It applies auto machine learning and image-processing methods, particularly convolutional neural network (CNN) style image modeling, to classify ecotypes from visual inputs more efficiently than manual approaches.\"}]","Auto machine learning tools to distinguish between two killer whale ecotypes | PDF",1785684454,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":28},"auto-machine-learning-tools-to-distinguish-between-two-killer-whale-ecotypes","",{"@graph":36,"@context":77},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/auto-machine-learning-tools-to-distinguish-between-two-killer-whale-ecotypes/118600/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"How does the study use machine learning for ecotype identification?","Question",{"text":75,"@type":76},"It applies auto machine learning and image-processing methods, particularly convolutional neural network (CNN) style image modeling, to classify ecotypes from visual inputs more efficiently than manual approaches.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,105,110,114,119,122,126],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":103,"slug":104},50,"technology",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},7,"Healthcare",40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":29,"slug":113},8,"Research & Report","research-report",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":98,"slug":129},19,"General","general"]