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The Pareto Principle - LLMs vs Nature šŸ§  Brain STREAM 2ļøāƒ£4ļøāƒ£

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Image of: Annette Raffan Annette Raffan

Table of Contents

Round 1

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Photo by Austin Distel on Unsplash
Happy Monday morning. Hope you like this new Brain STREAM newsletter format! Thought it was time I got organised in this respect. Iā€™m really enjoying exploring the topics in the recent newsletters. Annette

If youā€™re reading this newsletter, chances are youā€™ve heard of the Pareto Principle. In the spirit of this topic, I asked the AI component of Capacities to describe it to me.

ā€œThe Pareto Principle, also known as the 80/20 rule, states that roughly 80% of effects come from 20% of causes. It originated from an observation made by Italian economist Vilfredo Pareto in the early 20th century when he noticed that 80% of Italy's land was owned by 20% of the population.ā€

Fab, thanks AI! 80% of my work, with 20% of the effort.

The Pareto Principle seems to crop up everywhere - finance, populations, business, even nature - despite no-one knowing why. Itā€™s one of those mysteries of the universe.

Itā€™s not a rule because it has no known cause and effect.

So, chances are 80% of anything I do, will come from 20% of my effort anyway, because the 80/20 rule is just a natural phenomenon is it not?

Today I consider Large Language models and how I see them contributing to my own research work. If it is one thing LLMs excel at, itā€™s doing 80% of our work for us.

Round 1: Large Language Models

I was asking my sister if she uses Chat GPT in her line of work (business finance stuff). She said she uses it to do the first 70% of the work, creating marketing templates and business finance summaries, often through the use of a series of prompting questions.

I was curious to see some of the ways LLMs could help in research specifically, so I asked it ā€œfor examples of how academic researchers could use LLMs to apply the Pareto Principleā€:

  • Summarising key papers enabling you to find the 20% of research papers that will provide 80% of the background of your work
  • Finding the 20% most highly cited papers for 80% of the topic background
  • Analysing datasets and finding the 20% of the variables explaining 80% of the variance
  • Indicate the 20% of your hypotheses which are most likely to be significant (šŸ¤”)
  • Create text drafts containing the 20% of your results to explain 80% of your findings
  • List the top 20% of researchers in your field to optimise collaboration

Now whilst there may be some useful approaches contained in these examples Iā€™d be curious to know how it can predict the most significant hypotheses. Nevertheless, having the Pareto Principle in mind when conversing with an LLM is a worthwhile thing to do.

This is partly because I find they give answers that are 80% right. What I mean by this is they arenā€™t wrong but they arenā€™t 100% right either.


Hereā€™s an example from my own field (haha pun intended) with my comments written in bold:

Prompt: Please tell me the advantages and disadvantages of ley grasslands for soil structure

Answer: Certainly! Here are the specific advantages and disadvantages of ley grasslands for soil structure:

Advantages for Soil Structure

  1. Improved Soil Aggregation:

    • Root Systems: The robust (viz. perennial, usually, they are no more ā€˜robustā€™, in fact they probably change more over time) root systems of grasses and legumes (and herbs) in ley grasslands enhance soil aggregation (usually grasses specifically that are drivers of aggregation though not always), leading to improved soil structure.

    • Organic Matter: Decomposition of root biomass and plant residues increases organic matter content, which binds soil particles into stable aggregates. (Not only decomposition, but a lot of organic material is produced in root exudates)

    • (Wet-dry cycling of soils by roots also drives aggregation)

  2. Reduction of Soil Compaction:

    • Root Penetration: Deep root systems (mostly from those herbs you neglected to mention, alfalfa being an exception; the roots donā€™t have to be deep to alleviate surface compaction) help break up compacted layers of soil, improving aeration (depends on the species) and root growth for subsequent crops (in the subsoil only if you till afterwards which is often the case).

    • Earthworm Activity: Increased organic matter and root exudates encourage earthworm activity (as far as Iā€™m aware exudates do not encourage earthworm activity but they do contribute to organic matter), which further helps to alleviate compaction and improve soil porosity.

  3. Enhanced Water Infiltration and Retention:

    • Porosity: Improved soil structure (Roots and earthworms) increases the number of soil pores, facilitating better water infiltration (the ā€˜numberā€™ of pores is misleading, it is the volume, size distribution, connectivity and tortuosity of those pores that changes water infiltration. Itā€™s the equivalent of saying stronger people have more muscles - well they have the same number as everyone else, itā€™s just they have more fibres, better blood flow etc.).

    • Water Holding Capacity: Higher organic matter content enhances the soilā€™s ability to retain moisture, reducing runoff and improving drought resilience.

  4. Erosion Control:

    • Soil Stability: Dense root systems stabilize soil particles, protecting against erosion by wind and water.

    • Surface Cover: Continuous ground cover provided by grasses and legumes (lets just say ley grassland plant species) reduces the impact of raindrops and prevents soil erosion. (Surface cover also intercepts raindrops, reducing soil ponding and runoff)

You get the pictureā€¦


In my limited experience of using LLMs for my research, Iā€™ve generally found them rubbish. I tried Paperpal and it was nothing short of awful for a very simple question. It seemed to be obsessed about telling me about the microbial effects of plant roots when I specifically ask it about soil structural effects. Yes, microbial effects are relevant, but not within a 200 word answer.

Iā€™ve used Research Rabbit and other AI paper summarisers; useful maybe to make sure you havenā€™t missed something, but they are not up to date, so another 80% performance. To train it with the papers I need to give it to get an actually useful output, would require me to do some pretty decent literature searches, read the abstracts and well, hang on a minute - isnā€™t that what the AI is supposed to do?

Where I have found LLMs effective is to quickly and simply explain concepts, standard methods and R code. This is especially useful when an LLM is contained within your note-taking system (like Capacities). I could see me using them to help create lecture slides when I have a zillion other things to do at the same time.

If you donā€™t mind, Iā€™m going to big myself up a little here, and say that I work on the frontier of new knowledge. Whilst LLMs can interpret what has gone exceedingly well, even methodically structure new opportunities and approaches, it just doesnā€™t (yet) have the level of understanding in what Iā€™m working on. Iā€™m not sure how useful it is to summarise 80% of what has gone when often the devil is in the detail.

šŸ’” Action Point: Where could an LLM take away 80% of your work?

Round 2 next Monday, same time, same place



šŸ§Ŗ Sharpen Your Skills


šŸ§« Interesting Stuff

  • Ali Abdaal asks; Why have you not already achieved your goals? Is it your Goals, Plan or System thatā€™s failing you? Plus he says donā€™t become a full-time YouTuber, itā€™s just stressful; interesting coming from himā€¦ šŸ˜²
  • Donā€™t let eco-friendly messaging lead you into thinking food is better for you; Bryan Johnson compares dark chocolates for flavanoid and heavy metal levels; I wonder how many replicates they did? šŸ¤”

āŒ› In Case You Missed It



Quote of the Week

Author and historian Henry Hitchings on meetings in the book ā€˜Letā€™s Talkā€™ by Nihal Arthanayake:

"The meeting is ... a paradigm of what conversation is, and yet, the meeting is one of the lowest forms of civilisation, and is the enemy of thought and listening and getting anything done!"

What Iā€™m Reading

  • Favourite research paper of the week; Finn et al (2013); exploiting positive plant interactions makes for better insurance effects, causing almost unanimous overyielding in multi-species agricultural grasslands.
  • Letā€™s Talk - How to Have Better Conversations by Nihal Arthanayake; taking turns, actively listening and giving space are key to a good conversation.

I can't say how much it means to me for you to read (and hopefully subscribe toā€¦šŸ˜œ) this newsletter.

Thank you and I will see you next week.

Thanks for reading šŸ§  Brain STREAM ! Subscribe for free to receive new posts and support my work.

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Last Update: February 03, 2025

Author

Annette Raffan 79 Articles

Annette is a mum of one and a postgraduate researcher studying plant-soil interactions. She is innately curious, loves writing and making improvements to how she does research - and how you can too.

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