Machine Learning (ML) Platforms Can Contradict Dairy Scientists and Feed Firm Websites Regarding Dairy Cattle Performance from Feeding Seaweed Supplements
Siobhan O'Keefe, Rick Welsh, Mercy Oppong, Ryan Fitzgerald, David Conner, Michelle Tynan, Nichole Price, Charlotte Quigle
Choices, August 2024
Artificial intelligence through machine learning applications (hereafter ML) is emerging as a tool in evaluating, comparing, and going beyond human capabilities and knowledge. Despite the potential benefits of ML as a resource for answering scientific questions, such as those included in our analysis, some characteristics of ML-generated responses limit the interpretations of these results—such as ML “hallucinations”—of which researchers should be aware (McIntosh et al., 2023). Nonetheless, ML is quickly becoming a source for authoritative and trusted information on many topics (Knight, 2024; McIntosh et al., 2024), as university-based and other more rigorous research may be behind paywalls or otherwise difficult to access and as pay-to-play journals proliferate. Therefore, it is useful to conduct analyses comparing ML-generated information to traditionally trusted information sources, such as scientists’ observations, and to self-interested commercial information available to the public.
There has been growing interest in the dairy industry for algal feed supplements (AFS), such as Asparagopsis taxiformis, to be used in dairy cattle feed as an effective means of improving cattle health and productivity and reducing methane emissions (Moen, 2024; Tynan et al., 2023). Livestock feed company websites selling AFS list numerous health and environmental benefits from utilizing their dietary supplement in cattle feed. However, it is possible that scientific evidence and support among credentialed experts do not match the claims made on company websites or support for the findings produced by ML platforms. This paper compares the results generated by three commonly used ML platforms to survey results from 100 dairy scientists attending a Cornell Dairy Herd Health and Nutrition Conference in response to questions on the effectiveness of AFS as a supplement to improve herd health and productivity outcomes, and to claims of livestock feed firms on theirwebsites. Findings suggest that while ML may presenta viable resource for information, it should not be the primary source for extracting reliable information. No single source of information regarding seaweed feed supplements for dairy cattle should be the primary source; according to our findings, all forms of information may have some weaknesses.
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