G.E. Gardner, Clair Alston-Kno. Accreditation of new technologies for predicting intramuscular fat percentage: combining Bayesian models and industry rules for transparent decisions.. 2024. agriXiv.2024.00240
Accreditation of new technologies for predicting intramuscular fat percentage: combining Bayesian models and industry rules for transparent decisions.
DOI: 10.31220/agriRxiv.2024.00240
-
Abstract: Abstract The experiment evaluated a method for statistically assessing the accuracy of technologies that measure intramuscular fat percentage (IMF%), enabling referencing against accreditation accuracy thresholds. To compare this method to the existing rules-based industry standard we simulated data for 4 separate devices that predicted IMF% across a range between 0.5 - 9.5% for sheep meat. These devices were simulated to reflect increasingly inaccurate predictions, and the two methods for statistically assessing accuracy were then applied. We found that for the technology which only just meets the accreditation accuracy standards, as few as 25 samples were required within each quarter of the IMF% range to achieve 80% likelihood of passing accreditation. In contrast, using the rules based approach at least 200 samples were required within each quarter of the IMF% range, and this increased the likelihood of passing to only 50%. This method has been developed into an on-line analysis App, which commercial users can freely access to test the accuracy of their technologies.
Key words: imf; accreditation; rules; range; accuracy; requireSubmit time: 19 March 2024
Copyright: The copyright holder for this preprint is the author/funder, who has granted agriXiv a license to display the preprint in perpetuity. -
图表
-
Akhil Wilson, Raji Sukumar, N. Hemalath. Machine learning model for rice yield prediction using KNN regression.. 2021. doi: 10.31220/agriRxiv.2021.00070
Claudia Arndt, A.N. Hristov, William J. Price, Shelby C. McClelland, A.M. Pelaez, S.F. Cueva, J. Oh, A. Bannink, Ali Bayat, L.A. Crompton, J. Dijkstra, Maguy Eugène, E. Kebreab, M. Kreuzer, M. McGee, Christian Martin, C. J. Newbold, Christopher K. Reynolds, Angela Schwarm, Kevin J. Shingfield, Jolien B. Veneman, D. R. Yáñez-Ruíz, Zeren Y. Strategies to mitigate enteric methane emissions by ruminants - a way to approach the 2.0°C target.. 2021. doi: 10.31220/agriRxiv.2021.00040
Germán Wies, A. Monroy, Mario Ulises Pérez Zepeda, Luis García Barrios, Perla Xochitl Jaimes Piñón, José Miguel Cotes Torres, Pablo Fragoso Villavicencio, Marta Astie. Exploring the interaction of weed control management and crop structure on maize yield in the wide range of Mexican cropping systems.. 2024. doi: 10.31220/agriRxiv.2024.00252
Jesse M. Rubenstein, Philip E. Hulme, Christopher E. Buddenhagen, M.P. Rolston, J. G. Hampto. Weed seed contamination in imported seed lots entering New Zealand.. 2021. doi: 10.31220/agriRxiv.2021.00057
Jean‐Luc Jannink, Raul Astudillo, Peter I. Frazie. Insight into a two-part plant breeding scheme through Bayesian optimization of budget allocations.. 2023. doi: 10.31220/agriRxiv.2023.00188
Bryony Taylor, Jonathan Paul Casey, Sivapragasam Annamalai, Elizabeth A. Finch, Tim Beale, W. Holland, Sean T. Murphy, Cambria Finegol. Minimizing pest and disease risks in uncertain climates: CABI initiatives developing new technologies and tools for outreaching early warning to farmers.. 2021. doi: 10.31220/agriRxiv.2021.00064
Neal Haddaway, Melissa L. Rethlefsen, Melinda Davies, Julie Glanvill, Bethany McGowan, Kate Nyhan, Sarah Youn. A suggested data structure for transparent and repeatable reporting of bibliographic searching.. 2022. doi: 10.31220/agriRxiv.2022.00138
Robert L. Crabtree, Dean Koch, Subhash R. Lel. Misleading overestimation bias in methods to estimate wolf abundance that use spatial models.. 2023. doi: 10.31220/agriRxiv.2023.00215
Nuriel Shalom Mor, Tigabo Asras, Eli Gal, Tesfahon Demasia, Ezra Tarab, Nathaniel Ezekiel, Osher Nikapros, Oshri Semimufar, Eva Gladky, Maria Karpenko, Daniel Sason, Д. Н. Маслов, Omri Mo. Wine quality and type prediction from physicochemical properties using neural networks for machine learning: a free software for winemakers and customers.. 2022. doi: 10.31220/agriRxiv.2022.00125
Karthik Muthineni, Akhil Yalagonda, Praveen Gorla, Tarun Pullur. Implementation of automated motor starter unit for smart farming in India.. 2020. doi: 10.31220/agriRxiv.2020.00009
-
ID Submit time Number Download -
Public Anonymous To author only
Get Citation
Article Metrics
- Read: 71
- Download: 0
- Comment: 0



