{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T04:14:00Z","timestamp":1745986440093,"version":"3.40.4"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031788567"},{"type":"electronic","value":"9783031788574"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-78857-4_15","type":"book-chapter","created":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T05:34:57Z","timestamp":1736228097000},"page":"182-194","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Classification of Dairy Cattle Combined Milk Production and Methane Emission Levels via Multilabel & Multiclass Systems"],"prefix":"10.1007","author":[{"given":"Stephen","family":"Ross","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiying","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianhai","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masoud","family":"Shirali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huiru J.","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,8]]},"reference":[{"key":"15_CR1","unstructured":"FAO. Climate change and the global dairy cattle sector \u2013 The role of the dairy sector in a low-carbon future (2018). https:\/\/openknowledge.fao.org\/server\/api\/core\/bitstreams\/8749a956-0725-414f-8c35-58a5db0c2b5c\/content"},{"key":"15_CR2","unstructured":"FAO. How to Feed the World in 2050 (2024). www.fao.org\/fileadmin\/templates\/wsfs\/docs\/expert_paper\/How_to_Feed_the_World_in_2050.pdf"},{"key":"15_CR3","doi-asserted-by":"publisher","DOI":"10.3390\/foods6070053","author":"M Henchion","year":"2017","unstructured":"Henchion, M., Hayes, M., Mullen, A.M., Fenelon, M., Tiwari, B.: Future protein supply and demand: strategies and factors influencing a sustainable equilibrium. Foods (2017). https:\/\/doi.org\/10.3390\/foods6070053","journal-title":"Foods"},{"key":"15_CR4","unstructured":"USDA. Dairy: World Markets and Trade (2023). https:\/\/apps.fas.usda.gov\/psdonline\/circulars\/dairy.pdf"},{"key":"15_CR5","doi-asserted-by":"publisher","unstructured":"Yang, D.S., Jaja, I.F.: Culling and mortality of dairy cows: why it happens and how it can be mitigated. F1000Research (2021). https:\/\/doi.org\/10.12688\/f1000research.55519.2","DOI":"10.12688\/f1000research.55519.2"},{"key":"15_CR6","unstructured":"FAO. Pathways towards lower emissions \u2013 a global assessment of the greenhouse gas emissions and mitigation options from livestock agrifood systems. GLEAM3 dashboard (2023). https:\/\/foodandagricultureorganization.shinyapps.io\/GLEAMV3_Public\/"},{"key":"15_CR7","doi-asserted-by":"publisher","unstructured":"L\u00f3pez-Paredes J, et al.: Mitigation of greenhouse gases in dairy cattle via genetic selection: 1. Genetic parameters of direct methane using noninvasive methods and proxies of methane. J. Dairy Sci. (2020). https:\/\/doi.org\/10.3168\/jds.2019-17597","DOI":"10.3168\/jds.2019-17597"},{"key":"15_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.animal.2021.100294","author":"Y de Haas","year":"2021","unstructured":"de Haas, Y., Veerkamp, R.F., de Jong, G., Aldridge, M.N.: Selective breeding as a mitigation tool for methane emissions from dairy cattle. Animal (2021). https:\/\/doi.org\/10.1016\/j.animal.2021.100294","journal-title":"Animal"},{"key":"15_CR9","doi-asserted-by":"publisher","DOI":"10.3168\/jds.2020-19889","author":"CIV Manzanilla-Pech","year":"2021","unstructured":"Manzanilla-Pech, C.I.V., et al.: Breeding for reduced methane emission and feed-efficient Holstein cows: an international response. J. Dairy Sci. (2021). https:\/\/doi.org\/10.3168\/jds.2020-19889","journal-title":"J. Dairy Sci."},{"key":"15_CR10","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-16778-z","author":"H-Z Ghavi","year":"2022","unstructured":"Ghavi, H.-Z.: Estimates of the genetic contribution to methane emission in dairy cows: a meta-analysis. Sci. Rep. (2022). https:\/\/doi.org\/10.1038\/s41598-022-16778-z","journal-title":"Sci. Rep."},{"key":"15_CR11","doi-asserted-by":"publisher","unstructured":"Bo\u011fa, M., \u00c7evik, K.K., Burgut, A.: Classifying milk yield using deep neural network. Pakistan J. Zool. (2020). https:\/\/doi.org\/10.17582\/journal.pjz\/20190527090506","DOI":"10.17582\/journal.pjz\/20190527090506"},{"key":"15_CR12","doi-asserted-by":"publisher","DOI":"10.5455\/javar.2020.g438_","author":"H Radwan","year":"2020","unstructured":"Radwan, H., El Qaliouby, H., Abo Elfadl, E.: Classification and prediction of milk yield level for Holstein Friesian cattle using parametric and non-parametric statistical classification models. J. Adv. Vet. Anim. Res. (2020). https:\/\/doi.org\/10.5455\/javar.2020.g438_","journal-title":"J. Adv. Vet. Anim. Res."},{"key":"15_CR13","doi-asserted-by":"publisher","unstructured":"Yordanova, A., Gocheva-Ilieva, S., Kulina, H., Yordanova, L., Marinov, I.: Classification and regression tree analysis in modeling the milk yield and conformation traits for Holstein cows in Bulgaria. Agric. Sci. Technol. (2015). https:\/\/www.cabidigitallibrary.org\/doi\/pdf\/https:\/\/doi.org\/10.5555\/20153378818","DOI":"10.5555\/20153378818"},{"key":"15_CR14","doi-asserted-by":"crossref","unstructured":"Ross, S., Wang, H., Zheng, J.H., Yan, T., Shirali, M.: Approaches for predicting dairy cattle methane emissions: from traditional methods to machine learning. J. Anim. Sci. (Unpublished). Available on request (2023)","DOI":"10.1093\/jas\/skae219"},{"key":"15_CR15","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM.2016.7822764","author":"H Zheng","year":"2017","unstructured":"Zheng, H., Wang, H., Yan, T.: Modelling enteric methane emissions from milking dairy cows with Bayesian networks. BIBM (2017). https:\/\/doi.org\/10.1109\/BIBM.2016.7822764","journal-title":"BIBM"},{"key":"15_CR16","unstructured":"Mazzanti, S.: MRMR explained exactly how you wished someone explained to you. Towards Data Science (2021). https:\/\/towardsdatascience.com\/mrmr-explained-exactly-how-you-wished-someone-explained-to-you-9cf4ed27458b"},{"key":"15_CR17","unstructured":"Brownlee, J.: SMOTE for imbalanced classification with Python. Machine Learning Mastery (2021). https:\/\/machinelearningmastery.com\/smote-oversampling-for-imbalanced-classification\/"},{"key":"15_CR18","unstructured":"Karajgi, A.: Evaluating multi-label classifiers. Towards Data Science (2021). https:\/\/towardsdatascience.com\/evaluating-multi-label-classifiers-a31be83da6ea"}],"container-title":["Advances in Intelligent Systems and Computing","Advances in Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78857-4_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T11:20:13Z","timestamp":1745925613000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78857-4_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031788567","9783031788574"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78857-4_15","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"8 January 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"UKCI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"UK Workshop on Computational Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Belfast","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ukci2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}