Mitch Naughton
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mitchnaughtonphd.bsky.social
Mitch Naughton
@mitchnaughtonphd.bsky.social
700 followers 380 following 28 posts
PhD in Exercise Science | Lecturer in Exercise Physiology | Understanding Load, Fatigue, Recovery, and Systems Thinking
Posts Replies Media Videos
Reposted by Mitch Naughton
gregsasso.bsky.social
As a teacher, give me someone who wants to learn over basically any other trait
November 30, 2025 at 10:53 PM
Reposted by Mitch Naughton
danpsimpson.bsky.social
A PhD is a driving test. A postdoc is practice. You know that your students and postdocs will eventually be able to drive in directions you can’t or won’t. And it’s wonderfull. But it’s important not to mistake an unexciting work product with the actual value.
November 30, 2025 at 6:42 AM
Reposted by Mitch Naughton
wiringthebrain.bsky.social
Grateful to the Decoding the Gurus guys for having me on to discuss Autism, Microbiomes, & Mice Burying Marbles... @guruspod.bsky.social cc @statsepi.bsky.social @deevybee.bsky.social 😊 open.spotify.com/episode/01w8...
Autism, Microbiomes, & Mice Burying Marbles with Kevin Mitchell
open.spotify.com
November 19, 2025 at 9:32 AM
Reposted by Mitch Naughton
jphysiol.bsky.social
Recent #Research by Fabio G. Laginestra et al. examines the influence of biological sex on the #metabolic basis of skeletal muscle #fatigue in vivo 🔬 🧲

📜 Read the study here: physoc.onlinelibrary.wiley.com/doi/10.1113/...
November 19, 2025 at 12:02 PM
Reposted by Mitch Naughton
arc-tracker.bsky.social
Where to start, with a statement like this?

"Tim Ayres said the cuts were aimed at refocusing … CSIRO towards research priorities, such as critical minerals, iron & steel production in Australia."

From some rando down the bus stop, one would brush it off.

But this is from our Science Minister 🤯
CSIRO to cut up to 350 research jobs in major overhaul
After 440 positions were slashed last year, the CSIRO has announced more staff cuts across the country in a bid to remain financially viable.
www.abc.net.au
November 19, 2025 at 12:10 AM
Reposted by Mitch Naughton
statsepi.bsky.social
When I'm right, I'm right; and when I'm wrong, I'm just trying to create a teachable moment.
November 17, 2025 at 3:13 PM
Reposted by Mitch Naughton
mbeisen.bsky.social
Finally, someone has solved a real problem with AI! No more having to take a paper in the format for a journal that rejected you, and reformat it for a new journal. Well done!! formatmypaper.com
October 15, 2025 at 6:33 AM
Reposted by Mitch Naughton
rmcelreath.bsky.social
The real Butlerian Jihad is not againt AGI, but against AI slop? Happy weekend to all www.youtube.com/watch?v=_zfN...
AI Slop Is Destroying The Internet
YouTube video by Kurzgesagt – In a Nutshell
www.youtube.com
October 11, 2025 at 7:08 AM
Reposted by Mitch Naughton
josephmatthews.bsky.social
Okay I’m officially done on X/Twitter. In dire need of a healthy science community with people posting interesting takes on research. Where are my nutrition and metabolism people…? 🍎
a man drinking from a can that says science on it
ALT: a man drinking from a can that says science on it
media.tenor.com
October 3, 2025 at 6:52 PM
Reposted by Mitch Naughton
furbyhancock.bsky.social
Too late, Batman. Once this Tylenol floods the city's water supply, my wiki won't run out of editors ever again
September 22, 2025 at 9:54 PM
Reposted by Mitch Naughton
mattansb.msbstats.info
Finished marking papers in one of my courses. It's painful to see bright minds atrophied by the integration of LLMs into the "learning" process.

Instead of building an understanding, e.g. by trying to generalize examples seen in exercises, LLMs are used in an effort to "just do". Poorly.

Shame.
September 18, 2025 at 2:38 PM
Reposted by Mitch Naughton
craigagoodman1.bsky.social
The influence of biological sex on the metabolic basis of skeletal muscle fatigue in vivo
physoc.onlinelibrary.wiley.com/doi/10.1113/...
The influence of biological sex on the metabolic basis of skeletal muscle fatigue in vivo
Abstract figure legend Women are often reported to develop less muscle fatigue than men during physical activity. By combining electrical femoral nerve stimulation and phosphorus magnetic resonance s...
physoc.onlinelibrary.wiley.com
September 18, 2025 at 5:43 PM
Reposted by Mitch Naughton
drmarkburnley.bsky.social
You can see exactly where Wightman’s tank emptied, about 5-10 m from the line (by the Honda sign). If you calculate his critical speed and D’ from his outdoor PBs, D’ was gone 6 m before the line.
September 18, 2025 at 6:51 AM
Reposted by Mitch Naughton
arc-tracker.bsky.social
What do you think about Australia "associating with" Horizon Europe?

It's not me asking, but the Department of Industry, Science and Resources.

If you're interested, make a submission to their "request for information": ▶️ consult.industry.gov.au/association-...
Converlens - Engagement, insights and analytics platform for surveys and consultations
Collect, Manage and Analyse surveys, consultation data and text
consult.industry.gov.au
September 12, 2025 at 4:17 AM
Reposted by Mitch Naughton
olivia.science
Finally! 🤩 Our position piece: Against the Uncritical Adoption of 'AI' Technologies in Academia:
doi.org/10.5281/zeno...

We unpick the tech industry’s marketing, hype, & harm; and we argue for safeguarding higher education, critical
thinking, expertise, academic freedom, & scientific integrity.
1/n
Abstract: Under the banner of progress, products have been uncritically adopted or even imposed on users — in past centuries with tobacco and combustion engines, and in the 21st with social media. For these collective blunders, we now regret our involvement or apathy as scientists, and society struggles to put the genie back in the bottle. Currently, we are similarly entangled with artificial intelligence (AI) technology. For example, software updates are rolled out seamlessly and non-consensually, Microsoft Office is bundled with chatbots, and we, our students, and our employers have had no say, as it is not considered a valid position to reject AI technologies in our teaching and research. This is why in June 2025, we co-authored an Open Letter calling on our employers to reverse and rethink their stance on uncritically adopting AI technologies. In this position piece, we expound on why universities must take their role seriously toa) counter the technology industry’s marketing, hype, and harm; and to b) safeguard higher education, critical thinking, expertise, academic freedom, and scientific integrity. We include pointers to relevant work to further inform our colleagues. Figure 1. A cartoon set theoretic view on various terms (see Table 1) used when discussing the superset AI (black outline, hatched background): LLMs are in orange; ANNs are in magenta; generative models are in blue; and finally, chatbots are in green. Where these intersect, the colours reflect that, e.g. generative adversarial network (GAN) and Boltzmann machine (BM) models are in the purple subset because they are both generative and ANNs. In the case of proprietary closed source models, e.g. OpenAI’s ChatGPT and Apple’s Siri, we cannot verify their implementation and so academics can only make educated guesses (cf. Dingemanse 2025). Undefined terms used above: BERT (Devlin et al. 2019); AlexNet (Krizhevsky et al. 2017); A.L.I.C.E. (Wallace 2009); ELIZA (Weizenbaum 1966); Jabberwacky (Twist 2003); linear discriminant analysis (LDA); quadratic discriminant analysis (QDA). Table 1. Below some of the typical terminological disarray is untangled. Importantly, none of these terms are orthogonal nor do they exclusively pick out the types of products we may wish to critique or proscribe. Protecting the Ecosystem of Human Knowledge: Five Principles
September 6, 2025 at 8:13 AM
Reposted by Mitch Naughton
penders.bsky.social
Our data stewards have started recommending that we no longer use US-based infrastructure for #openscience practices, given the risk of (near-future) censorship, from pre-print and data hosting to preregistration and more. That includes OSF.
September 3, 2025 at 8:22 AM
Reposted by Mitch Naughton
adamjkucharski.bsky.social
Results so stunningly clear they inspired this classic xkcd (xkcd.com/2400/):
September 1, 2025 at 7:32 PM
Reposted by Mitch Naughton
jyoungman.bsky.social
I don't get how people don't realize that a technology that replaces junior and entry level positions in a field destroys that field.

There's no skipping steps. You have to be a junior before you can be a senior, and if you don't have senior people, you don't have a field.
scalzi.com John Scalzi @scalzi.com · Aug 31
Remember, kids: A real big goal of "AI" is to entirely sever capital from labor, and no, there will be no universal basic income, you can all just starve and die, thanks

www.axios.com/2025/08/26/a...
AI is already taking jobs away from entry-level workers
Software and customer service are most at risk right now and could be the canary in the coal mine.
www.axios.com
September 1, 2025 at 4:03 AM
Reposted by Mitch Naughton
gregatki.bsky.social
It was a rewarding labour of love to work on this paper with a fantastic team of authors, “Understanding Treatment Response Heterogeneity Using Crossover Randomized Controlled Trials: A Primer for Exercise and Nutrition Scientists”. @hk-ijsnem.bsky.social journals.humankinetics.com/view/journal...
journals.humankinetics.com
August 28, 2025 at 5:50 PM
Reposted by Mitch Naughton
captain7bag.bsky.social
Amazing!: fpca.paperplayground.app From
@aartgoossens.bsky.social
a Paper Playground paperplayground.app implementation of the FPCA model. It's a great concept to interactively showcase research results. cc
@drphilipskiba.bsky.social
August 30, 2025 at 10:19 PM
Reposted by Mitch Naughton
donmoyn.bsky.social
So far, the main businesses that have been financially benefited from AI have been the producers of blue books
www.wsj.com/business/cha...
They Were Every Student’s Worst Nightmare. Now Blue Books Are Back.
Cheating with ChatGPT has become a huge problem for colleges. The solution is painfully old-school.
www.wsj.com
August 26, 2025 at 8:41 PM
Reposted by Mitch Naughton
dingdingpeng.the100.ci
Ever stared at a table of regression coefficients & wondered what you're doing with your life?

Very excited to share this gentle introduction to another way of making sense of statistical models (w @vincentab.bsky.social)
Preprint: doi.org/10.31234/osf...
Website: j-rohrer.github.io/marginal-psy...
Models as Prediction Machines: How to Convert Confusing Coefficients into Clear Quantities Abstract Psychological researchers usually make sense of regression models by interpreting coefficient estimates directly. This works well enough for simple linear models, but is more challenging for more complex models with, for example, categorical variables, interactions, non-linearities, and hierarchical structures. Here, we introduce an alternative approach to making sense of statistical models. The central idea is to abstract away from the mechanics of estimation, and to treat models as “counterfactual prediction machines,” which are subsequently queried to estimate quantities and conduct tests that matter substantively. This workflow is model-agnostic; it can be applied in a consistent fashion to draw causal or descriptive inference from a wide range of models. We illustrate how to implement this workflow with the marginaleffects package, which supports over 100 different classes of models in R and Python, and present two worked examples. These examples show how the workflow can be applied across designs (e.g., observational study, randomized experiment) to answer different research questions (e.g., associations, causal effects, effect heterogeneity) while facing various challenges (e.g., controlling for confounders in a flexible manner, modelling ordinal outcomes, and interpreting non-linear models). Figure illustrating model predictions. On the X-axis the predictor, annual gross income in Euro. On the Y-axis the outcome, predicted life satisfaction. A solid line marks the curve of predictions on which individual data points are marked as model-implied outcomes at incomes of interest. Comparing two such predictions gives us a comparison. We can also fit a tangent to the line of predictions, which illustrates the slope at any given point of the curve. A figure illustrating various ways to include age as a predictor in a model. On the x-axis age (predictor), on the y-axis the outcome (model-implied importance of friends, including confidence intervals). Illustrated are 1. age as a categorical predictor, resultings in the predictions bouncing around a lot with wide confidence intervals 2. age as a linear predictor, which forces a straight line through the data points that has a very tight confidence band and 3. age splines, which lies somewhere in between as it smoothly follows the data but has more uncertainty than the straight line.
August 25, 2025 at 11:49 AM
Reposted by Mitch Naughton
mattansb.msbstats.info
I must have spent dozens of hour (possibly literally) finalizing the figures for my PhD thesis with this color wizard - it was really fun! (and not just because it took my mind off writer's block -- in the pre-chatGPT times)

hclwizard.org
weare.rladies.org We Are R-Ladies @weare.rladies.org · Aug 25
Are you like me and struggle to choose colors for your charts?
I recently found the colorspace package, and it's a total game-changer.
cran.r-project.org/web/packages...

Load the package, run 'pal<-choose_palette()' and a GUI shows you color combos! Check out my example with #TidyTuesday data:
Positron IDE open showing R code and colorspace GUI
August 25, 2025 at 6:48 PM
Reposted by Mitch Naughton
captain7bag.bsky.social
For anyone that wants a look under the hood of the FPCA paper and model from me and @drphilipskiba.bsky.social the r script and mmp data are available here: drive.google.com/drive/folder...

journals.humankinetics.com/view/journal...
FPCA_F3_Model - Google Drive
drive.google.com
August 24, 2025 at 4:08 PM