Matthew Yates

Ludicrous. Absolutely ludicrous, especially coming from such a respected institution.

I’m honestly flabbergasted that this even occurred. The idea of an LLM acting as a SA survivor or a trained crisis councillor and then giving real people advice without them knowing the origin of that advice? Worse, being actively deceived about the origin of that advice?

BUT EVEN IF that weren’t true, this is a gross violation of informed consent, which is a component of research ethics that is *extremely important*.

Before anyone tries to justify this on utilitarian grounds, the research is of dubious quality because Reddit is so riddled with bots that a substantial amount of their LLM interactions could have been bots talking to bots. So the data are fundamentally flawed from the get-go.

How the hell the University ethics review process let this go is beyond me, but apparently the university is defending it?

Nobody in the online community had any idea this was going on. The community also has mechanisms to score whether users were ‘convinced’ by the arguments of a poster (in this case AI). The LLM had more than 100 such comments that received this score. These are (theoretically, at least) real people.

See:

www.reddit.com/r/changemyvi...

The researchers had the LLMs pose as users with expertise or experience in various subject areas (including SA) who then provided advice or arguments to users posting in the subreddit.

What in the absolute f*ck

Apparently researchers at the University of Zurich conducted an experiment in the /r/changemyview subreddit where they assessed the ability of large language models to change people’s views.

This was allegedly done without any informed consent whatsoever.

eDNAjoint: An R package for interpreting paired or semi‐paired environmental DNA and traditional survey data in a Bayesian framework Environmental DNA (eDNA) sampling is increasingly used in surveys of species distribution as a potentially sensitive and efficient monitoring method. Yet access to modelling tools designed specifi...

Just stumbled across this, another solid paper from the Kelly lab! Great to see work like this on making eDNA interpretation more accessible to end-users

besjournals.onlinelibrary.wiley.com/doi/10.1111/...

Environmental DNA Haplotyping Reveals Dispersal Patterns of Invasive Bluegill Sunfish, Lepomis macrochirus, in Japan We developed an environmental DNA (eDNA)-based haplotyping approach to reveal the haplotype distributions of invasive bluegill sunfish in Japan. Our analysis detected a negative relationship between ...

Well this is just a super cool paper, using eDNA to track dispersal dynamics of invasive fish species based on haplotype number:

doi.org/10.1002/edn3...

Edit: Post 6 should say 'note that you take the mean AFTER scaling with 'b'

And there you have it! Pretty happy with how this turned out, was a long collaboration with lots of different folks - huge thanks to Dr. Taylor Wilcox, Dr. Shannon Kay, Dr. Pedro Peres-Neto (@comecology.bsky.social), and Dr. Daniel Heath, who all were instrumental in developing this paper!

20) But slope values for eDNA/biomass relationships will be higher for protists compared to bacteria due to their larger cell sizes.

19) Amongst single-cellular organisms, ‘b’ will equal 0 since we expect eDNA to scale with numerical abundance (each cell contains ~approximately~ the same number of gene copies).

18) Compared to macro-organisms, eDNA/biomass slopes will be very low since capturing organisms whole produces lots of eDNA.

17) Amongst multicellular microorganisms (e.g., plankton, microinvertebrates) ‘b’ will equal 1, as we expect eDNA to scale with biomass due to most eDNA being derived from capturing organisms whole-body.

16) Plants and turtles? Hard external surfaces and low metabolism -> high eDNA/biomass slopes compared to metabolically active groups like fish.

15) Amongst macro-organisms ‘b’ will likely be close to ~0.75, and eDNA/biomass regression slopes are likely to be predominantly determined by metabolics and surface area characteristics.

14) More broadly, the framework also lays out a foundation for modelling these relationships across taxonomic groups; we can thus make predictions about what the relative values of the beta coefficients in the relationship and the ‘b’ scaling parameters will look like across taxonomic groups.

13) If only one of those relationships produces a good regression, then that means you probably need to directly try to estimate the value of ‘b’ in your system!

12) If you correlate eDNA with biomass, you are assuming a ‘b’ of 1 -> that inherently means that eDNA divided by mean population mass should correlate with organism abundance. If it doesn’t, you have a problem!

11) If you correlate eDNA with N, you are assuming a ‘b’ of 0 -> that inherently means that eDNA multiplied by mean population mass should correlate well with biomass. If it doesn’t, you have a problem!

10) This is what most studies have done – previous researchers typically presented the ‘best-fit’ relationship for either eDNA/biomass or eDNA/abundance. We show that either assumption REQUIRES that adjusted eDNA should reflect the other variable – you should always present BOTH relationships

9) Key take-home: Assuming eDNA correlates with numerical abundance assumes an allometric scaling coefficient of 0, and assuming eDNA correlates with biomass assumes an allometric scaling coefficient of 1.

8) This can be achieved by jointly estimating both the allometric scaling coefficient (‘b’) AND jointly estimating the beta coefficient of the regression, while driving the regression through the origin.

7) What’s REALLY cool is that the algebra shows that the two regressions (eDNA/N + eDNA/biomass) should share the same value of the beta coefficient (slope). Furthermore, predicted N and biomass must also satisfy the relationship that N multiplied by the mean mass of a population = biomass

6) We algebraically demonstrate that eDNA data should scale with numerical abundance by dividing quantitative eDNA data by the mean of the mass values in a population raised to the power of an allometric scaling coefficient, ‘b’ (not that you take the mean AFTER scaling to ‘b’).

5) We provide a series of equations you can ‘adjust’ eDNA data with to simultaneously reflect both numerical abundance and biomass. This is based on theory from metabolic ecology, which has developed allometric scaling frameworks to model metabolic relationships, which we extend to eDNA.

4) To predict numerical abundance and biomass from eDNA data, you have to have eDNA modelled on the x-axis in both relationships. This provides a bit of a conundrum – you’re trying to predict two variables from one!