{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T16:20:52Z","timestamp":1761582052914,"version":"3.40.3"},"publisher-location":"Cham","reference-count":12,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030659646"},{"type":"electronic","value":"9783030659653"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-65965-3_2","type":"book-chapter","created":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T19:48:52Z","timestamp":1612208932000},"page":"26-37","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Reasoning About Neural Network Activations: An Application in Spatial Animal Behaviour from Camera Trap Classifications"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5342-5895","authenticated-orcid":false,"given":"Benjamin C.","family":"Evans","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5105-3506","authenticated-orcid":false,"given":"Allan","family":"Tucker","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8258-3534","authenticated-orcid":false,"given":"Oliver R.","family":"Wearn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9253-3765","authenticated-orcid":false,"given":"Chris","family":"Carbone","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,2]]},"reference":[{"key":"2_CR1","unstructured":"Beery, S., Morris, D., Yang, S.: Efficient pipeline for camera trap image review. arXiv:1907.06772 [cs.CV] (2019). https:\/\/arxiv.org\/abs\/1907.06772"},{"key":"2_CR2","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North (2019). https:\/\/doi.org\/10.18653\/v1\/n19-1423","DOI":"10.18653\/v1\/n19-1423"},{"key":"2_CR3","doi-asserted-by":"publisher","unstructured":"Franco, C., Hepburn, L.A., Smith, D.J., Nimrod, S., Tucker, A.: A Bayesian belief network to assess rate of changes in coral reef ecosystems. Environ. Model. Softw. 80, 132\u2013142 (2016). https:\/\/doi.org\/10.1016\/j.envsoft.2016.02.029. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S1364815216300494","DOI":"10.1016\/j.envsoft.2016.02.029"},{"key":"2_CR4","doi-asserted-by":"publisher","unstructured":"Glover-Kapfer, P., Soto-Navarro, C.A., Wearn, O.R.: Camera-trapping version 3.0: current constraints and future priorities for development. Remote Sens. Ecol. Conserv. 5(3), 209\u2013223 (2019). https:\/\/doi.org\/10.1002\/rse2.106. https:\/\/zslpublications.onlinelibrary.wiley.com\/doi\/abs\/10.1002\/rse2.106","DOI":"10.1002\/rse2.106"},{"key":"2_CR5","doi-asserted-by":"publisher","unstructured":"Maldonado, A., Uusitalo, L., Tucker, A., Blenckner, T., Aguilera, P., Salmer\u00f3n, A.: Prediction of a complex system with few data: evaluation of the effect of model structure and amount of data with dynamic Bayesian network models. Environ. Model. Softw. 118, 281\u2013297 (2019). https:\/\/doi.org\/10.1016\/j.envsoft.2019.04.011. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S1364815218310338","DOI":"10.1016\/j.envsoft.2019.04.011"},{"key":"2_CR6","unstructured":"van den Oord, A., et al.: Wavenet: a generative model for raw audio. arXiv:1609.03499 [cs.SD] (2016). https:\/\/arxiv.org\/abs\/1609.03499"},{"key":"2_CR7","doi-asserted-by":"publisher","unstructured":"Rowcliffe, J.M., Carbone, C.: Surveys using camera traps: are we looking to abrighter future? Anim. Conserv. 11(3), 185\u2013186 (2008). https:\/\/doi.org\/10.1111\/j.1469-1795.2008.00180.x. https:\/\/zslpublications.onlinelibrary.wiley.com\/doi\/abs\/10.1111\/j.1469-1795.2008.00180.x","DOI":"10.1111\/j.1469-1795.2008.00180.x"},{"issue":"3","key":"2_CR8","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211\u2013252 (2015). https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int. J. Comput. Vision"},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Spirtes, P., Glymour, C., Scheines, R.: Causation, prediction, and search (1993)","DOI":"10.1007\/978-1-4612-2748-9"},{"key":"2_CR10","doi-asserted-by":"publisher","unstructured":"Trifonova, N., Kenny, A., Maxwell, D., Duplisea, D., Fernandes, J., Tucker, A.: Spatio-temporal Bayesian network models with latent variables for revealingtrophic dynamics and functional networks in fisheries ecology. Ecol. Inf. 30, 142\u2013158 (2015). https:\/\/doi.org\/10.1016\/j.ecoinf.2015.10.003. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S1574954115001648","DOI":"10.1016\/j.ecoinf.2015.10.003"},{"key":"2_CR11","doi-asserted-by":"publisher","unstructured":"Uusitalo, L.: Advantages and challenges of Bayesian networks in environmentalmodelling. Ecol. Model. 203(3), 312\u2013318 (2007). https:\/\/doi.org\/10.1016\/j.ecolmodel.2006.11.033. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0304380006006089","DOI":"10.1016\/j.ecolmodel.2006.11.033"},{"key":"2_CR12","doi-asserted-by":"publisher","unstructured":"Xie, S., Girshick, R., Dollar, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017. https:\/\/doi.org\/10.1109\/cvpr.2017.634","DOI":"10.1109\/cvpr.2017.634"}],"container-title":["Communications in Computer and Information Science","ECML PKDD 2020 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-65965-3_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T11:34:25Z","timestamp":1619264065000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-65965-3_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030659646","9783030659653"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-65965-3_2","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"2 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ghent","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Belgium","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecmlpkdd2020.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"945","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"195","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4,5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4,4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference took place virtually due to the COVID-19 pandemic","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}