Palimpsest

Evolving notes, images and sounds by Luis Apiolaza

Page 3 of 72

Me holding Rex Bernardo's book

You realise that New Zealand is small when…

2024-08-13 /

I was chatting with a colleague (Salvador Gezan) in April about teaching, learning and books, and he suggested “have a look at Rex Bernardo’s book”. I searched for it in my university library, no luck. I thought “well, just ask for it as an interlibrary loan. Surely we can borrow a copy from another university in the country and get it in a few days”.

I forgot all about the interloan for three months (!), when I received an email from my university library, saying something like “Hey, we couldn’t find the book you were looking for in NZ. However, we ordered a copy for you”. Then another email today, “Hey, please come and pick up your book from the central library”.

And here I am, at the library with THE copy of the book. We are that small in NZ.

P.S. Salvador and I both did our undergrad at the Universidad de Chile, just with a few years difference.

Kowhai trees at university

The best part of LinkedIn

2024-08-01 /

After “professional Twitter’s” demise I joined LinkedIn (less than a year ago) to keep in touch with colleagues. Overall, I like the posts from people I chose to follow and dislike most of the “suggested by the algorithm” motivational, HR, marketing, leadership, etc. posts.

However, the best part, at least for me, is to see updates by our Forestry students. Looking at their new jobs, either in New Zealand or very far away. Pictures in the office, dealing with tree establishment, forest fires, forest management, processing, etc. It makes me happy to feel even tangentially connected to their new experiences outside the university. Cheers to all of them.

Image from https://doi.org/10.1016/j.tplants.2018.02.001

Breeding trade-offs

2024-07-25 /

On one side, it is obvious what we should do: increase any of the values in the numerator (selection intensity, accuracy and genetic variability) or reduce the denominator (how long it takes us to deliver gain). Any of those changes will increase genetic gain per year.

However, the world is full of trade-offs. First, that equation is for a single trait and our breeding programmes deal with multiple traits, so we are selecting on an index that combines the genetic information for all traits (their genetic variability, heritabilities, and correlations) with their relative economic value. Not all the traits have the same value for industry. And not all the traits cost the same to assess: measuring an external characteristic, say size, is a lot easier than measuring internal characteristics, say chemical composition.

Perhaps it is convenient to sacrifice accuracy, using a second- or third-best method for phenotyping, if we can assess more cheaply and quickly (increasing selection intensity). Perhaps it is convenient to clone our testing material (reducing effective population size), so we genotype once but test in multiple environments for multiple traits. Or we can redefine the traits, so we are not trying to predict a specific value but just check if we meet technical/quality thresholds.

There are many other options and that’s why the (more general version of the) breeder’s equation is central in what we do. It permits us to play with ideas, run alternatives and adapt our breeding programmes to whatever conditions we are facing. Sometimes it is super-duper high-throughput hyperspectral drone-enabled goodness. Sometimes is low-budget el-quicko back-of-a-workshop “appropriate” technology. Same equation, same decisions.

Audrey Hepburn being all multilanguage and agnostic.

Start with the programming language and statistical approach used by your community

2024-07-22 /

I have been very busy with the start of the semester, teaching regression modelling. The craziest thing was that the R installation was broken in the three computer labs I was allocated to use. It would not have been surprising if I were talking about Python ( 🤣 ), but the installation script had a major bug. Argh!

Anyhow, I was talking with a student who was asking me why we were using R in the course (she already knew how to use Python). If you work in research for a while, particularly in statistics/data analysis, you are bound to bump onto long-lived discussions. It isn’t the Text Editor Wars nor the Operating Systems wars. I am referring to two questions that come up all the time in long threads:

  1. What language should I learn or use for my analyses?
  2. Should I be a Bayesian or a Frequentist? You are supposed to choose a statistical church.

The easy answer for the first one is “because I say so”: it’s my course. A longer answer is that a Domain Specific Language makes life a lot easier, as it is optimised to tasks performed in that domain. An even longer answer points to something deeper: a single language is never enough. My head plays images of Minitab, SAS, Genstat, Splus, R, ASReml, etc that I had to use at some point just to deal with statistics. Or Basic, Fortran, APL (crazy, I know), Python, Matlab, C++, etc that I had to use as more general languages at some point. The choice of language will depend on the problem and the community/colleagues you end up working with. Along your career you become a polyglot.

As an agnostic (in my good days) or an atheist (in my bad ones) I am not prone to join churches. In my research, I tend to use mostly frequentist stats (of the REML persuasion) but, sometimes, Bayesian approaches feel like the right framework. In most of my problems both schools tend to give the same, if not identical results.

I have chosen to be an interfaith polyglot.

Cybersyn replica in Centro Cultural La Moneda.

The purpose of a system is what it does (POSIWID)

2024-07-18 /

This is a popular* dictum by systems theorist Stafford Beer, pointing out that the self-described purpose of a system (or an organisation) is not the same as its actual purpose. I am often reminded of POSIWID when companies or universities state their “values” but then we contrast them with what they actually value, via their applications of carrots and sticks.

Famously, Google used “Don’t be evil” in their corporate code of conduct, but fired employees complaining about the ethics of their AI projects. Or your organisation states that employee wellbeing is a priority, but it uses an “ambulance at the bottom of a cliff” approach; there is no prevention, but instead you are told to use mindfulness and meditation to reduce stress.

I tend to be sceptical about people and organisations insisting too much on their values; I rather see their results, which tend to reflect their true purpose in what they do.

*Popular in the sense of nerd popular, not pop-star popular.

Note: In the early 1970s Stafford Beer was involved in the development of Cybersyn, an attempt to plan the whole Chilean economy from a room connected to industry via 500 telex machines. Replica of Cybersyn in Centro Cultural La Moneda, Santiago.

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