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Institute of

SF Sea Fisheries

Non peer-reviewed publications

  1. 0

    Denechaud C, Mahé K, Aanestad Godiksen J, Abdullah M, Adamack A, Andrialovanirina N, Arnfred Bojesen T, Bekaert K, Berg F, Blaszkowski J, Brückner H, Carbonara P, Cayetano A, Chin A, Choudhury I, Cruz R, Davies JO, Denis J, Diack G, Stransky C, et al (2026) Workshop on emerging methods and technologies for the automated analysis of calcified structures (WKETAC). Copenhagen: ICES, iii, 32 p, ICES Sci Rep 8(7), DOI:10.17895/ices.pub.31161292

    https://literatur.thuenen.de/digbib_extern/dn070933.pdf

  2. 1

    Cayetano A (2024) arjaycc/ai_otolith : v1.2.1 [Datenpublikation]. 1 ZIP archive. Genève: Zenodo, DOI:10.5281/zenodo.10684464

  3. 2

    Cayetano A (2024) arjaycc/ai_otolith: version 1.2. updated [Datenpublikation] [online]. Genève: Zenodo, zu finden in <https://github.com/arjaycc/ai_otolith/tree/v1.2.0> [zitiert am 04.12.2024], DOI:10.5281/zenodo.10732415

  4. 3

    Cayetano A, Stransky C (2024) OtolithenKI versions (portable) [Datenpublikation] [online]. Version 1. Genève: Zenodo, zu finden in <https://zenodo.org/records/10954471> [zitiert am 04.12.2024], DOI:10.5281/zenodo.10954470

  5. 4

    Cayetano A, Stransky C (2024) OtolithenKI versions (portable) [Datenpublikation] [online]. Version 2. Genève: Zenodo, zu finden in <https://zenodo.org/records/13821024> [zitiert am 04.12.2024], DOI:10.5281/zenodo.13821024

  6. 5

    Cayetano A (2023) arjaycc/ai_otolith : automating the process of fish age reading using Mask R-CNN and U-Net [Datenpublikation]. 1 ZIP archive; v2. Genève: Zenodo, DOI:10.5281/zenodo.10001755

  7. 6

    Cayetano A (2023) arjaycc/ai_otolith : fish age reading using M-RCNN and U-Net [Datenpublikation]. 1 ZIP archive; Version v0.9. Genève: Zenodo, DOI:10.5281/zenodo.8341298

  8. 7

    Cayetano A (2023) arjaycc/ai_otolith : version 1.2 [Datenpublikation]. 1 ZIP archive. Genève: Zenodo, DOI:10.5281/zenodo.10225909

  9. 8

    Cayetano A, Stransky C, Krumme U (2023) Baltic Dataset (Thuenen Institute of Baltic Sea Fisheries) [Datenpublikation] [online]. Genève: Zenodo, zu finden in <https://zenodo.org/records/8341149> [zitiert am 04.12.2024], DOI:10.5281/zenodo.8341149

  10. 9

    Cisewski B, Cayetano A, Kühn B, Stransky C, Sulanke E (2023) Fishing for pattern - KI in der Fischereiforschung. In: KIDA-Fachtagung, 27. - 28. September 2023, Quedlinburg : Abstractbuch. Braunschweig: Geschäftsstelle Think Tank Digitalisierung, Johann Heinrich von Thünen-Institut, pp 47-48

  11. 10

    Cayetano A, Stransky C (2023) North Sea Dataset (Thuenen Institute of Sea Fisheries) [Datenpublikation] [online]. Genève: Zenodo, zu finden in <https://zenodo.org/records/8341092> [zitiert am 04.12.2024], DOI:10.5281/zenodo.8341092

  12. 11

    Irisson J-O, Malde K, Beltran O, Cayetano A, Fernandes-Salvador JA, Gomes A, Jamet C, Kiko R, Kühn B, Moustahfid H, Möller KO, Politikos D, Romagnan J-B, Sauzède R, Seydi V, Sokolova M, Watson JT (2022) Working Group on Machine Learning in Marine Science (WGMLEARN; outputs from 2021 meeting). Copenhagen: ICES, iii, 16 p, ICES Sci Rep 4(15), DOI:10.17895/ices.pub.10060

    https://literatur.thuenen.de/digbib_extern/dn070033.pdf

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