Mastodawn

More AI garbage

I’m indebted to a post on Mastodon for drawing my attention to a blog post about a paper with the title Bridging the gap: explainable ai for autism diagnosis and parental support with TabPFNMix and SHAP that appeared in the journal Nature Scientific Reports (which claims to be peer-reviewed).

Here is Figure 1 of that paper:

I’m no expert on Autism Diagnosis, but I’m pretty sure that neither “Fexcectorn” nor “frymblal” (medical or otherwise) nor “runctitional” are words in the English language. Why do the person’s legs go through the table? And why is Autism represented by a bicycle? This nonsensical figure was clearly generated by AI, as is much of the text of the paper. How on Earth did this crap pass peer review?

Still, Nature Scientific Reports is index in Scopus, which we all know is a watertight guarantee of quality…

P.S. The article was published on 19th November 2025. It is now prefaced by an Editor’s Note: “Readers are alerted that the contents of this paper are subject to criticisms that are being considered by editors. A further editorial response will follow the resolution of these issues.”

#autism #genai #natureScientificReports #scopus

ℵ₀ 🏳️‍⚧️🏴‍☠️Jul 10, 2023

#Fanzor is a #eukaryotic #programmable #RNA-guided #endonuclease

https://www.nature.com/articles/s41586-023-06356-2

#biology #geneticengineering #crisprcas #CRISPR #naturescientificreports

Fanzor is a eukaryotic programmable RNA-guided endonuclease - Nature

Fanzor is shown to be an RNA-guided DNA endonuclease, demonstrating that such endonucleases are found in all domains of life and indicating a potential new tool for genome engineering applications.

Nature

Patrick Weber 🌍🇪🇺🇱🇺Jul 16, 2021

Great to see @DJBackes present 4 years of his work and get his well deserved PhD! What a journey, onwards and upwards now

You missed out though the small detail of your co-authorship in #NatureScientificReports ! https://www.nature.com/articles/s41598-021-86650-z
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RT @NTeferle
@uni_lu PhD defence of @DJBackes, leading to a high resolution digital terrain model for Tristan da Cunha island. Thanks to Robin Repetto and other data prov…
https://twitter.com/NTeferle/status/1416031227686096900

Towards global flood mapping onboard low cost satellites with machine learning - Scientific Reports

Spaceborne Earth observation is a key technology for flood response, offering valuable information to decision makers on the ground. Very large constellations of small, nano satellites— ’CubeSats’ are a promising solution to reduce revisit time in disaster areas from days to hours. However, data transmission to ground receivers is limited by constraints on power and bandwidth of CubeSats. Onboard processing offers a solution to decrease the amount of data to transmit by reducing large sensor images to smaller data products. The ESA’s recent PhiSat-1 mission aims to facilitate the demonstration of this concept, providing the hardware capability to perform onboard processing by including a power-constrained machine learning accelerator and the software to run custom applications. This work demonstrates a flood segmentation algorithm that produces flood masks to be transmitted instead of the raw images, while running efficiently on the accelerator aboard the PhiSat-1. Our models are trained on WorldFloods: a newly compiled dataset of 119 globally verified flooding events from disaster response organizations, which we make available in a common format. We test the system on independent locations, demonstrating that it produces fast and accurate segmentation masks on the hardware accelerator, acting as a proof of concept for this approach.